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Senan, Ebrahim Mohammed, Mukti E. Jadhav, Taha H. Rassem, Abdulaziz Salamah Aljaloud, Badiea Abdulkarem Mohammed, and Zeyad Ghaleb Al-Mekhlafi. "Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning." Computational and Mathematical Methods in Medicine 2022 (May 18, 2022): 1–17. http://dx.doi.org/10.1155/2022/8330833.
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Cancer is considered one of the most aggressive and destructive diseases that shortens the average lives of patients. Misdiagnosed brain tumours lead to false medical intervention, which reduces patients’ chance of survival. Accurate early medical diagnoses of brain tumour are an essential point for starting treatment plans that improve the survival of patients with brain tumours. Computer-aided diagnostic systems have provided consecutive successes for helping medical doctors make accurate diagnoses and have conducted positive strides in the field of deep and machine learning. Deep convolutional layers extract strong distinguishing features from the regions of interest compared with those extracted using traditional methods. In this study, different experiments are performed for brain tumour diagnosis by combining deep learning and traditional machine learning techniques. AlexNet and ResNet-18 are used with the support vector machine (SVM) algorithm for brain tumour classification and diagnosis. Brain tumour magnetic resonance imaging (MRI) images are enhanced using the average filter technique. Then, deep learning techniques are applied to extract robust and important deep features via deep convolutional layers. The process of combining deep and machine learning techniques starts, where features are extracted using deep learning techniques, namely, AlexNet and ResNet-18. These features are then classified using SoftMax and SVM. The MRI dataset contains 3,060 images divided into four classes, which are three tumours and one normal. All systems have achieved superior results. Specifically, the AlexNet+SVM hybrid technique exhibits the best performance, with 95.10% accuracy, 95.25% sensitivity, and 98.50% specificity.
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Sowrirajan, Saran Raj, and Surendiran Balasubramanian. "Brain Tumor Classification Using Machine Learning and Deep Learning Algorithms." International Journal of Electrical and Electronics Research 10, no. 4 (December 30, 2022): 999–1004. http://dx.doi.org/10.37391/ijeer.100441.
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Early identification and diagnosis of brain tumors have been a difficult problem. Many approaches have been proposed using machine learning techniques and a recent study has explored deep learning techniques which are the subset of machine learning. In this analysis, Feature extraction techniques such as GLCM, Haralick, GLDM, and LBP are applied to the Brain tumor dataset to extract different features from MRI images. The features which have been extracted from the MRI brain tumor dataset are trained using classification algorithms such as SVM, Decision Tree, and Random Forest. Performances of traditional algorithms are analyzed using the accuracy metric and stated that LBP with SVM produces better classification accuracy of 84.95%. Brain tumor dataset is input to three-layer convolutional neural network and performance has been analyzed using accuracy which is of 93.10%. This study proves that CNN performs well over the machine learning algorithms considered in this work.
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Taie, Shereen A., and Wafaa Ghonaim. "A new model for early diagnosis of alzheimer's disease based on BAT-SVM classifier." Bulletin of Electrical Engineering and Informatics 10, no. 2 (April 1, 2021): 759–66. http://dx.doi.org/10.11591/eei.v10i2.2714.
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Magnetic Resonance Images (MRI) of the Brain is a significant tool to diagnosis Alzheimer's disease due to its ability to measure regional changes in the brain that reflect disease progression to detect early stages of the disease. In this paper, we propose a new model that adopts Bat for parameter optimization problem of Support vector machine (SVM) to diagnose Alzheimer’s disease via MRI biomedical image. The proposed model uses MRI for biomedical image classification to diagnose three classes; normal controls (NC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The proposed model based on segmentation for the most involved areas in the disease hippocampus, the features of MRI brain images are extracted to build feature vector of the brain, then extracting the most significant features in neuroimaging to reduce the high dimensional space of MRI images to lower dimensional subspace, and submitted to machine learning classification technique. Moreover, the model is applied on different datasets to validate the efficiency which show that the new Bat-SVM model can yield promising acceptable level of accuracy reached to 95.36 % using maximum number of bats equal to 50 and number of generation equal to 10.
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Rezaei, Mansour, Ehsan Zereshki, Soodeh Shahsavari, Mohammad Gharib Salehi, and Hamid Sharini. "Prediction of Alzheimer’s Disease Using Machine Learning Classifiers." International Electronic Journal of Medicine 9, no. 3 (September 30, 2020): 116–20. http://dx.doi.org/10.34172/iejm.2020.21.
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Background: Alzheimer’s disease (AD) is the most common brain failure for which no cure has yet been found. The disease starts with a disturbance in the brain structure and then it manifests itself clinically. Therefore, by timely and correct diagnosis of changes in the structure of the brain, the occurrence of this disease or at least its progression can be prevented. Due to the fact that magnetic resonance imaging (MRI) can be used to obtain very useful information from the brain, and also because it is non-invasive, this method has been considered by researchers. Materials and Methods: The data were obtained from an MRI database (MIRIAD) of 69 subjects including 46 AD patients and 23 healthy controls (HC). Individuals were categorized based on two criteria including NINCDS-ADRAD and MMSE, as the gold standard. In this paper, we used the support vector machine (SVM) and Bayesian SVM classifiers. Results: Using the SVM classifier with Gaussian radial basis function (RBF) kernel, we distinguished AD and HC with an accuracy of 88.34%. The most important regions of interest (ROIs) in this study included right para hippocampal gyrus, left para hippocampal gyrus, right hippocampus, and left hippocampus. Conclusion: This study showed that the SVM model with Gaussian RBF kernel can distinguish AD from HC with high accuracy. These studies are of great importance in medical science. Based on the results of this study, MRI centers and neurologists can perform AD screening tests in people over the age of 50 years.
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Mohammed, Badiea Abdulkarem, Ebrahim Mohammed Senan, Taha H. Rassem, Nasrin M. Makbol, Adwan Alownie Alanazi, Zeyad Ghaleb Al-Mekhlafi, Tariq S. Almurayziq, and Fuad A. Ghaleb. "Multi-Method Analysis of Medical Records and MRI Images for Early Diagnosis of Dementia and Alzheimer’s Disease Based on Deep Learning and Hybrid Methods." Electronics 10, no. 22 (November 20, 2021): 2860. http://dx.doi.org/10.3390/electronics10222860.
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Dementia and Alzheimer’s disease are caused by neurodegeneration and poor communication between neurons in the brain. So far, no effective medications have been discovered for dementia and Alzheimer’s disease. Thus, early diagnosis is necessary to avoid the development of these diseases. In this study, efficient machine learning algorithms were assessed to evaluate the Open Access Series of Imaging Studies (OASIS) dataset for dementia diagnosis. Two CNN models (AlexNet and ResNet-50) and hybrid techniques between deep learning and machine learning (AlexNet+SVM and ResNet-50+SVM) were also evaluated for the diagnosis of Alzheimer’s disease. For the OASIS dataset, we balanced the dataset, replaced the missing values, and applied the t-Distributed Stochastic Neighbour Embedding algorithm (t-SNE) to represent the high-dimensional data in the low-dimensional space. All of the machine learning algorithms, namely, Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbours (KNN), achieved high performance for diagnosing dementia. The random forest algorithm achieved an overall accuracy of 94% and precision, recall and F1 scores of 93%, 98% and 96%, respectively. The second dataset, the MRI image dataset, was evaluated by AlexNet and ResNet-50 models and AlexNet+SVM and ResNet-50+SVM hybrid techniques. All models achieved high performance, but the performance of the hybrid methods between deep learning and machine learning was better than that of the deep learning models. The AlexNet+SVM hybrid model achieved accuracy, sensitivity, specificity and AUC scores of 94.8%, 93%, 97.75% and 99.70%, respectively.
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Refaat, Fatma M., M. M. Gouda, and Mohamed Omar. "Detection and Classification of Brain Tumor Using Machine Learning Algorithms." Biomedical and Pharmacology Journal 15, no. 4 (December 20, 2022): 2381–97. http://dx.doi.org/10.13005/bpj/2576.
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The brain is the organ that controls the activities of all parts of the body. The tumor is familiar as an irregular outgrowth of tissue. Brain tumors are an abnormal lump of tissue in which cells grow up and redouble uncontrollably. It is categorized into different types based on their nature, origin, growth rate, and stage of progress. Detection of the tumor by traditional methods is time-consuming and does not widen to diagnose a large amount of data and is less accurate. So, the automatic diagnosis of the tumors in the brain by magnetic resonance imaging (MRI) plays a very important role in computer-aided diagnosis. This paper concentrates on the diagnosis of three kinds of brain tumors (a meningioma, a glioma, and a pituitary tumor). Machine learning algorithms: KNN, SVM, and GRNN are suggested to increase accuracy and reduce diagnostic time by using a publicly available dataset, features that are extracted of images, data pre-processing methods, and the principal component analysis (PCA). This paper aims to minimize the training time of the suggested algorithms. The dimensionality reducing technique is applied to the dataset and diagnosis using machine learning algorithms, such as Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Generalized Regression Neural Networks (GRNN). The accuracies of the algorithms used in diagnosing tumors are 97%, 96.24%, and 94.7% for KNN, SVM, and GRNN, respectively. The KNN is therefore regarded as the algorithm of choice.
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Prado, Julio José, and Ignacio Rojas. "Machine Learning for Diagnosis of Alzheimer’s Disease and Early Stages." BioMedInformatics 1, no. 3 (December 13, 2021): 182–200. http://dx.doi.org/10.3390/biomedinformatics1030012.
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According to the WHO, approximately 50 million people worldwide have dementia and there are nearly 10 million new cases every year. Alzheimer’s disease is the most common form of dementia and may contribute to 60–70% of cases. It has been proved that early diagnosis is key to promoting early and optimal management. However, the early stage of dementia is often overlooked and patients are typically diagnosed when the disease progresses to a more advanced stage. The objective of this contribution is to predict Alzheimer’s early stages, not only dementia itself. To carry out this objective, different types of SVM and CNN machine learning classifiers will be used, as well as two different feature selection algorithms: PCA and mRMR. The different experiments and their performance are compared when classifying patients from MRI images. The newness of the experiments conducted in this research includes the wide range of stages that we aim to predict, the processing of all the available information simultaneously and the Segmentation routine implemented in SPM12 for preprocessing. We will make use of multiple slices and consider different parts of the brain to give a more accurate response. Overall, excellent results have been obtained, reaching a maximum F1 score of 0.9979 from the SVM and PCA classifier.
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Hassan, Mosaad W., Arabi Keshk, Amira Abd El-atey, and Elham Alfeky. "BRAIN STROKE DETECTION USING TENSOR FACTORIZATION AND MACHINE LEARNING MODELS." International Journal of Engineering Technologies and Management Research 8, no. 8 (August 16, 2021): 1–12. http://dx.doi.org/10.29121/ijetmr.v8.i8.2021.1006.
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Stroke is one of the foremost common disorders among the elderly. Early detection of stroke from Magnetic Resonance Imaging (MRI) is typically based on the representation method of these images. Representing MRI slices in two dimensional structures (matrices) implies ignoring the dependencies between these slices. Additionally, to combine all features exist in these slices requires more computations and time. However, this results in inexact diagnosis. In this paper, we propose a new tensor-based approach for stroke detection from MRI. The proposed methodology has two phases. In first phase, each patient’s MRI are represented as a tensor. Tensor representations are powerful because they capture the dependencies in high-dimensional data, MRI of patient, which gives more reliable and accurate results. Also, tensor factorization is used as a method for feature extraction and reduction, which improves the performance and accuracy of classifiers. In second phase, these extracted features are used to train support vector machine (SVM) and XGBoost classifiers to classify MRI images into normal and abnormal. The proposed method is assessed with MRI dataset, and the conducted experiments illustrate the efficiency of this approach. It achieves classification accuracy of 98%.
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Wu, Wentao, Daning Li, Jiaoyang Du, Xiangyu Gao, Wen Gu, Fanfan Zhao, Xiaojie Feng, and Hong Yan. "An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm." Computational and Mathematical Methods in Medicine 2020 (July 14, 2020): 1–10. http://dx.doi.org/10.1155/2020/6789306.
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Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutional network model has the problems of a large number of parameters and large loss of information in the encoding and decoding process. This paper proposes a deep convolutional neural network fusion support vector machine algorithm (DCNN-F-SVM). The proposed brain tumor segmentation model is mainly divided into three stages. In the first stage, a deep convolutional neural network is trained to learn the mapping from image space to tumor marker space. In the second stage, the predicted labels obtained from the deep convolutional neural network training are input into the integrated support vector machine classifier together with the test images. In the third stage, a deep convolutional neural network and an integrated support vector machine are connected in series to train a deep classifier. Run each model on the BraTS dataset and the self-made dataset to segment brain tumors. The segmentation results show that the performance of the proposed model is significantly better than the deep convolutional neural network and the integrated SVM classifier.
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D Bonde, Girish, and Dr Manish Jain. "Analysis of MRI Data of Brain for CAD System." International Journal of Engineering & Technology 7, no. 2.17 (April 15, 2018): 63. http://dx.doi.org/10.14419/ijet.v7i2.17.11560.
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Magnetic resonance imaging (MRI) technologies are currently one of the most effective tools in the diagnosis of a wide variety of socially significant pathologies including cancer, arteriosclerosis, episodes. Ischemic and neurodegenerative diseases [1, 2, 3, 4].This paper gives detailed idea of pre-processing, and segmentation(FCM, soft and hard) of MRI brain tumor images. This paper also insights the machine learning(SOM, NN and SVM) approach for automatic classification(PTPSA, fBM) of brain tissues. Different performance evaluation parameter and similarity metrics are discuss to define the efficiency of computer-aided diagnostic (CAD) system.
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Anantharajan, Shenbagarajan, Shenbagalakshmi Gunasekaran, and Thavasi Subramanian. "MRI brain image analysis using deep learning techniques and multi-class support vector machine." International journal of health sciences 6, S1 (March 21, 2022): 1699–708. http://dx.doi.org/10.53730/ijhs.v6ns1.4925.
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In recent times, an identification and classification of brain tumour become more essential to save human life. Brain tumour detection is considered most challenging problem and many researchers are finding optimized solution for early diagnosis. It occurs because of the irrepressible growth of cells in the brain and classified as malignant and benign tumour. In this research work, an automatic brain tumour detection system using CNN with Softmax and CNN with Multiclass SVM (M-SVM). It was clearly comprehend that the correct learning procedures and matching must yield perfect results. A database of the medical image was complex to divide. Classifying and identifying brain tumour a novel learning procedure, the combination of CNN and M-SVM were used to classify the input MRI Brin image is tumour or non-tumour. This Proposed method evaluated by the fig share dataset and proves the proposed method produced high accuracy. Evaluation and testing of the process used 5 fold validation process with Harvard, Radiopaedia and Figshare dataset. The proposed methods evaluated using Figshare dataset and classifier produced classification accuracy of 98.9% of CNN with Softmax and produced an accuracy of 99.2% of CNN with M-SVM.
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Chen, Xuguang, Vishwa Parekh, Luke peng, Michael Chan, Michael Soike, Emory McTyre, Michael Jacobs, and Lawrence Kleinberg. "RADT-14. COMPARISON OF MACHINE LEARNING ALGORITHMS IN DISTINGUISHING RADIATION NECROSIS FROM PROGRESSION OF BRAIN METASTASES TREATED WITH STEREOTACTIC RADIOSURGERY (SRS)." Neuro-Oncology 22, Supplement_2 (November 2020): ii184. http://dx.doi.org/10.1093/neuonc/noaa215.767.
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Abstract PURPOSE To test the effectiveness of machine learning algorithms in distinguishing radiation necrosis (RN) from tumor progression (TP) using MRI radiomic features. METHODS Brain metastases were treated with SRS to a median dose of 18Gy. Lesions that showed evidence of progression on follow-up MRI were sampled surgically, and diagnoses confirmed by histopathology. Cases from 2 institutions were combined and randomly assigned for training (70%) and testing (30%). T1 post-contrast (T1c) and T2 fluid attenuated inversion recovery (T2 FLAIR) MRI were used for radiomic feature extraction (50 features each). Three subsets of radiomic features were obtained and tested: Signature #1 included 10 previously published features that correlated with diagnosis on T test; signature #2 and #3 included 5 and 12 features obtained through recursive elimination using random forest (RF) and support vector machine (SVM), respectively. Supervised machine learning models were trained using RF, SVM (radial kernel) and regularized discriminant analysis (RDA) algorithms based on all three radiomics signatures. Receiver operator characteristics (ROC) were compared between signatures and algorithms. RESULTS A total of 135 individual lesions (37 RN and 98 TP) were included. Signature #3 demonstrated the highest area under the curve in the training set (average AUC=0.98, vs 0.95 and 0.92 for signature #1 and #2), as well as the testing set (average AUC=0.83, vs 0.74 and 0.79 for signature #1 and #2). RF and SVM demonstrated similar performance in both training (average AUC 0.99-1) and testing datasets (average AUC 0.79-0.80) among all three signatures. Both RF and SVM were superior to RDA in performance (average training AUC 0.83, testing AUC 0.77). The greatest sensitivity (83%) and specificity (100%) in the testing set were achieved using signature #3 and SVM. CONCLUSION RF and SVM are effective in distinguishing RN from TP in a multi-institution dataset using radiomic signatures.
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Ali, Muhammad Umair, Karam Dad Kallu, Haris Masood, Shaik Javeed Hussain, Safee Ullah, Jong Hyuk Byun, Amad Zafar, and Kawang Su Kim. "A Robust Computer-Aided Automated Brain Tumor Diagnosis Approach Using PSO-ReliefF Optimized Gaussian and Non-Linear Feature Space." Life 12, no. 12 (December 6, 2022): 2036. http://dx.doi.org/10.3390/life12122036.
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Brain tumors are among the deadliest diseases in the modern world. This study proposes an optimized machine-learning approach for the detection and identification of the type of brain tumor (glioma, meningioma, or pituitary tumor) in brain images recorded using magnetic resonance imaging (MRI). The Gaussian features of the image are extracted using speed-up robust features (SURF), whereas its non-linear features are obtained using KAZE, owing to their high performance against rotation, scaling, and noise problems. To retrieve local-level information, all brain MRI images are segmented into an 8 × 8 pixel grid. To enhance the accuracy and reduce the computational time, the variance-based k-means clustering and PSO-ReliefF algorithms are employed to eliminate the redundant features of the brain MRI images. Finally, the performance of the proposed hybrid optimized feature vector is evaluated using various machine learning classifiers. An accuracy of 96.30% is obtained with 169 features using a support vector machine (SVM). Furthermore, the computational time is also reduced to 1 min compared to the non-optimized features used for training of the SVM. The findings are also compared with previous research, demonstrating that the suggested approach might assist physicians and doctors in the timely detection of brain tumors.
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Gautam, Suruchi, Sweety Ahlawat, and Prabhat Mittal. "Binary and Multi-class Classification of Brain Tumors using MRI Images." International Journal of Experimental Research and Review 29 (December 30, 2022): 1–9. http://dx.doi.org/10.52756/ijerr.2022.v29.001.
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A dangerous and potentially fatal condition is a brain tumor. Early detection of this disease is critical for determining the best course of treatment. Tumor detection and classification by human inspection is a time consuming, error-prone task involving huge amounts of data. Computer-assisted machine learning and image analysis techniques have achieved significant results in image processing. In this study, we use supervised and deep learning classifiers to detect and classify tumors using the MRI images from the BRATS 2020 dataset. At the outset, the proposed system classifies images as healthy or normal brains and brain having tumorous growth. We employ four supervised machine learning classifiers SVM, Decision tree, Naïve Bayes and Linear Regression, for the binary classification. Highest accuracy (96%) was achieved with SVM and DT, with SVM giving a better Recall rate of 98%. Thereafter, categorization of the tumor as Pituitary adenoma, Meningioma, or Glioma, is performed using supervised (SVM, DT) classifiers and a 6-layer Convolution Neural Network. CNN performs better than the other classifiers, with a 93% accuracy and 92% recall rate. The suggested system is employable as a powerful decision-support tool to assist radiologists and oncologists in clinical diagnosis without requiring invasive procedures like a biopsy.
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Feng, Wei, Nicholas Van Halm-Lutterodt, Hao Tang, Andrew Mecum, Mohamed Kamal Mesregah, Yuan Ma, Haibin Li, et al. "Automated MRI-Based Deep Learning Model for Detection of Alzheimer’s Disease Process." International Journal of Neural Systems 30, no. 06 (May 27, 2020): 2050032. http://dx.doi.org/10.1142/s012906572050032x.
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In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imaging technique, was applied in this study to evaluate its contribution to improving the diagnostic accuracy of AD. Three-dimensional convolutional neural networks (3D-CNNs) were applied with magnetic resonance imaging (MRI) to execute binary and ternary disease classification models. The dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used to compare the deep learning performances across 3D-CNN, 3D-CNN-support vector machine (SVM) and two-dimensional (2D)-CNN models. The outcomes of accuracy with ternary classification for 2D-CNN, 3D-CNN and 3D-CNN-SVM were [Formula: see text]%, [Formula: see text]% and [Formula: see text]% respectively. The 3D-CNN-SVM yielded a ternary classification accuracy of 93.71%, 96.82% and 96.73% for NC, MCI and AD diagnoses, respectively. Furthermore, 3D-CNN-SVM showed the best performance for binary classification. Our study indicated that ‘NC versus MCI’ showed accuracy, sensitivity and specificity of 98.90%, 98.90% and 98.80%; ‘NC versus AD’ showed accuracy, sensitivity and specificity of 99.10%, 99.80% and 98.40%; and ‘MCI versus AD’ showed accuracy, sensitivity and specificity of 89.40%, 86.70% and 84.00%, respectively. This study clearly demonstrates that 3D-CNN-SVM yields better performance with MRI compared to currently utilized deep learning methods. In addition, 3D-CNN-SVM proved to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners. This suggests that it can potentially be exploited by untrained operators and extended to virtual patient imaging data. Furthermore, owing to the safety, noninvasiveness and nonirradiative properties of the MRI modality, 3D-CNN-SMV may serve as an effective screening option for AD in the general population. This study holds value in distinguishing AD and MCI subjects from normal controls and to improve value-based care of patients in clinical practice.
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Lilhore, Umesh Kumar, Sarita Simaiya, Devendra Prasad, and Kalpna Guleria. "A Hybrid Tumour Detection and Classification Based on Machine Learning." Journal of Computational and Theoretical Nanoscience 17, no. 6 (June 1, 2020): 2539–44. http://dx.doi.org/10.1166/jctn.2020.8927.
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Every excess tissue or impaired production of brain tissue in the human embryo is known as something of a tumor. Inside the brain, there may have been a tumor or any other orifice. Recognition of tumors and proper treatment at all times are still a difficult challenge. MRI devices are used mostly for the identification of specific tumors. MRI technologies are most often used for either the identification of specific tumors. Use artificial intelligence, medical diagnosis by imaging and machine learning is considered one of the many important issues for systems. Brain tumor evaluation generally requires greater accuracy, although small differences in assessment may turn to hazards. Because of this, the segmentation of both the tumor is a serious medical obstacle. Here proposed work introduces a hybrid machine learning-based tumor detection system (HMLBTD) for MR frames. The Fuzzy C-Means and K-Means Clustering Composite Clustering methodology have been used by the proposed HMLBTD frameworks and subsequently improved the classification of SVM and classification of normal and abnormal tumors. Across clustering, throughout order to achieve statistically valid performance, HMLBTD incorporates Fuzzy C-Means hybrid versions to achieve precision and K-means through segmentation. Throughout the second clustering step, HMLBTD employs Enhanced SVM (and use the ADA-boost framework with SVM) As well as the suggested HMLBTD strategy and also the proposed solution being implemented by utilizing different performance descriptive statistics using the MATLAB framework. An experimental study demonstrates that HMLBTD’s novel approach delivers higher yields than those of the traditional methods.
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Li, Yan, Zuhao Ge, Zhiyan Zhang, Zhiwei Shen, Yukai Wang, Teng Zhou, and Renhua Wu. "Broad Learning Enhanced 1H-MRS for Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus." Computational and Mathematical Methods in Medicine 2020 (November 22, 2020): 1–13. http://dx.doi.org/10.1155/2020/8874521.
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In this paper, we explore the potential of using the multivoxel proton magnetic resonance spectroscopy (1H-MRS) to diagnose neuropsychiatric systemic lupus erythematosus (NPSLE) with the assistance of a support vector machine broad learning system (BL-SVM). We retrospectively analysed 23 confirmed patients and 16 healthy controls, who underwent a 3.0 T magnetic resonance imaging (MRI) sequence with multivoxel 1H-MRS in our hospitals. One hundred and seventeen metabolic features were extracted from the multivoxel 1H-MRS image. Thirty-three metabolic features selected by the Mann-Whitney U test were considered to have a statistically significant difference ( p < 0.05 ). However, the best accuracy achieved by conventional statistical methods using these 33 metabolic features was only 77%. We turned to develop a support vector machine broad learning system (BL-SVM) to quantitatively analyse the metabolic features from 1H-MRS. Although not all the individual features manifested statistics significantly, the BL-SVM could still learn to distinguish the NPSLE from the healthy controls. The area under the receiver operating characteristic curve (AUC), the sensitivity, and the specificity of our BL-SVM in predicting NPSLE were 95%, 95.8%, and 93%, respectively, by 3-fold cross-validation. We consequently conclude that the proposed system effectively and efficiently working on limited and noisy samples may brighten a noinvasive in vivo instrument for early diagnosis of NPSLE.
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AlSaeed, Duaa, and Samar Fouad Omar. "Brain MRI Analysis for Alzheimer’s Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning." Sensors 22, no. 8 (April 11, 2022): 2911. http://dx.doi.org/10.3390/s22082911.
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Alzheimer’s disease is the most common form of dementia and the fifth-leading cause of death among people over the age of 65. In addition, based on official records, cases of death from Alzheimer’s disease have increased significantly. Hence, early diagnosis of Alzheimer’s disease can increase patients’ survival rates. Machine learning methods on magnetic resonance imaging have been used in the diagnosis of Alzheimer’s disease to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on MRI images is complicated, requiring the involvement of an expert user. Therefore, implementing deep learning as an automatic feature extraction method could minimize the need for feature extraction and automate the process. In this study, we propose a pre-trained CNN deep learning model ResNet50 as an automatic feature extraction method for diagnosing Alzheimer’s disease using MRI images. Then, the performance of a CNN with conventional Softmax, SVM, and RF evaluated using different metric measures such as accuracy. The result showed that our model outperformed other state-of-the-art models by achieving the higher accuracy, with an accuracy range of 85.7% to 99% for models with MRI ADNI dataset.
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Wang, Fang, Fang Wen, Jingran Liu, Junjuan Yan, Liping Yu, Ying Li, and Yonghua Cui. "Classification of tic disorders based on functional MRI by machine learning: a study protocol." BMJ Open 12, no. 5 (May 2022): e047343. http://dx.doi.org/10.1136/bmjopen-2020-047343.
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IntroductionTic disorder (TD) is a common neurodevelopmental disorder in children, and it can be categorised into three subtypes: provisional tic disorder (PTD), chronic motor or vocal TD (CMT or CVT), and Tourette syndrome (TS). An early diagnostic classification among these subtypes is not possible based on a new-onset tic symptom. Machine learning tools have been widely used for early diagnostic classification based on functional MRI (fMRI). However, few machine learning models have been built for the diagnostic classification of patients with TD. Therefore, in the present study, we will provide a study protocol that uses the machine learning model to make early classifications of the three different types of TD.Methods and analysisWe planned to recruit 200 children aged 6–9 years with new-onset tic symptoms and 100 age-matched and sex-matched healthy controls under resting-state MRI scanning. Based on the neuroimaging data of resting-state fMRI, the support vector machine (SVM) model will be built. We planned to construct an SVM model based on functional connectivity for the early diagnosis classification of TD subtypes (including PTD, CMT/CVT, TS).Ethics and disseminationThis study was approved by the ethics committee of Beijing Children’s Hospital. The trial results will be submitted to peer-reviewed journals for publication.Trial registration numberChiCTR2000033257.
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Kang, Jaeyong, Zahid Ullah, and Jeonghwan Gwak. "MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers." Sensors 21, no. 6 (March 22, 2021): 2222. http://dx.doi.org/10.3390/s21062222.
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Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.
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Aslam, Nida, Irfan Ullah Khan, Asma Bashamakh, Fatima A. Alghool, Menna Aboulnour, Noorah M. Alsuwayan, Rawa’a K. Alturaif, Samiha Brahimi, Sumayh S. Aljameel, and Kholoud Al Ghamdi. "Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities." Sensors 22, no. 20 (October 16, 2022): 7856. http://dx.doi.org/10.3390/s22207856.
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Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients’ data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.
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Chen, ZhiHong, Tao Yan, ErLei Wang, Hong Jiang, YiQian Tang, Xi Yu, Jian Zhang, and Chang Liu. "Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning." Computational Intelligence and Neuroscience 2020 (March 18, 2020): 1–13. http://dx.doi.org/10.1155/2020/6405930.
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Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. This study proposed an ML framework based on coarse-to-fine feature selection. The proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. The method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. The experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. The identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.
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Wahlang, Imayanmosha, Arnab Kumar Maji, Goutam Saha, Prasun Chakrabarti, Michal Jasinski, Zbigniew Leonowicz, and Elzbieta Jasinska. "Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age." Sensors 22, no. 5 (February 24, 2022): 1766. http://dx.doi.org/10.3390/s22051766.
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Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal. Gender and age are added as higher attributes for more accurate and meaningful classification. A deep learning Convolutional Neural Network (CNN)-based technique and a Deep Neural Network (DNN) are also proposed for effective classification. Other deep learning architectures such as LeNet, AlexNet, ResNet, and traditional approaches such as SVM are also implemented to analyze and compare the results. Age and gender biases are found to be more useful and play a key role in classification, and they can be considered essential factors in brain tumor analysis. It is also worth noting that, in most circumstances, the proposed technique outperforms both existing SVM and AlexNet. The overall accuracy obtained is 88% (LeNet Inspired Model) and 80% (CNN-DNN) compared to SVM (82%) and AlexNet (64%), with best accuracy of 100%, 92%, 92%, and 81%, respectively.
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Shang, Qun, Qi Zhang, Xiao Liu, and Lingchen Zhu. "Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm." Computational and Mathematical Methods in Medicine 2022 (May 6, 2022): 1–11. http://dx.doi.org/10.1155/2022/3144035.
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This research was aimed at discussing the application value of different machine learning algorithms in the prediction of early Alzheimer’s disease (AD), which was based on hippocampal volume changes in magnetic resonance imaging (MRI). In the research, the 84 cases in American Alzheimer’s disease neuroimaging initiative (ADNI) database were selected as the research data. Based on the scoring results of cognitive function, all cases were divided into three groups, including cognitive function normal (normal group), early mild cognitive impairment (e-MCI group), and later mild cognitive impairment (l-MCI group) groups. Each group included 28 cases. The features of hippocampal volume changes in MRI images of the patients in different groups were extracted. The samples of training set and test set were established. Besides, the established support vector machine (SVM), decision tree (DT), and random forest (RF) prediction models were used to predict e-MCI. Metalinear regression was utilized to analyze MRI feature data, and the predictive accuracy, sensitivity, and specificity of different models were calculated. The result showed that the volumes of hippocampal left CA1, left CA2-3, left CA4-DG, left presubiculum, left tail, right CA2-3, right CA4-DG, right presubiculum, and right tail in e-MCI group were all smaller than those in normal group ( P < 0.01 ). The corresponding volume of hippocampal subregions in l-MCI group was remarkably reduced compared with that in normal group ( P < 0.001 ). The volumes of regions left CA1, left CA2-3, left CA4-DG, right CA2-3, right CA4-DG, and right presubiculum were all positively correlated with logical memory test-delay recall (LMT-DR) score ( R 2 = 0.1702 , 0.3779, 0.1607, 0.1620, 0.0426, and 0.1309; P < 0.001 ). The predictive accuracy of training set sample by DT, SVM, and RF was 86.67%, 93.33%, and 98.33%, respectively. Based on the changes in the volumes of left CA4-DG, right CA2-3, and right CA4-DG, the predictive accuracy of e-MCI and l-MCI by RF model was both higher than those by DT model ( P < 0.01 ). Besides, the predictive accuracy, sensitivity, and specificity of e-MCI by RF model was all notably higher than those by DT model ( P < 0.01 ). The above results demonstrated that the effective early AD prediction models were established by the volume changes in hippocampal subregions, which was based on RF in the research. The establishment of early AD prediction models offered certain reference basis to the diagnosis and treatment of AD patients.
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Daimiel Naranjo, Isaac, Peter Gibbs, Jeffrey S. Reiner, Roberto Lo Gullo, Caleb Sooknanan, Sunitha B. Thakur, Maxine S. Jochelson, et al. "Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis." Diagnostics 11, no. 6 (May 21, 2021): 919. http://dx.doi.org/10.3390/diagnostics11060919.
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The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018–March 2020; Medical University Vienna, from January 2011–August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7–99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70–0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75–0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77–0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0–88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies.
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Mesin, Luca, Francesco Ponzio, Christian Francesco Carlino, Matteo Lenge, Alice Noris, Maria Carmela Leo, Michela Sica, Kathleen McGreevy, Erica Leila Ahngar Fabrik, and Flavio Giordano. "A Machine Learning Approach to Support Treatment Identification for Chiari I Malformation." Applied Sciences 12, no. 18 (September 8, 2022): 9039. http://dx.doi.org/10.3390/app12189039.
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Chiari I malformation is characterized by the herniation of cerebellar tonsils below the foramen magnum. It is often accompanied by syringomyelia and neurosurgical management is still controversial. In fact, it is frequent that some symptomatic patients initially undergo bony decompression of the posterior fossa and need in a short time more invasive surgery with higher morbility (e.g., decompression of posterior fossa with dural plastic, with or without tonsillar coarctation) because of unsatisfactory results at MRI controls. This study proposes a machine learning approach (based on SVM classifier), applied to different morphometric indices estimated from sagittal MRI and some information on the patient (i.e., age and symptoms at diagnosis), to recognize patients with higher risk of syringomyelia and clinical deterioration. Our database includes 58 pediatric patients who underwent surgery treatment. A negative outcome at 1 year from the intervention was observed in 38% of them (accuracy of 62%). Our algorithm allows us to increase the accuracy to about 71%, showing it to be a valid support to neurosurgeons in refining the clinical picture.
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Li, Qi, and Mary Qu Yang. "Comparison of machine learning approaches for enhancing Alzheimer’s disease classification." PeerJ 9 (February 25, 2021): e10549. http://dx.doi.org/10.7717/peerj.10549.
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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.
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Zhu, Jinlong, Xiujian Hu, Chao Zhang, and Guanglei Sheng. "Multi-View Modeling Method for Functional MRI Images." Journal of Medical Imaging and Health Informatics 11, no. 2 (February 1, 2021): 432–36. http://dx.doi.org/10.1166/jmihi.2021.3300.
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This paper proposes a new unsupervised fuzzy feature mapping method based on fMRI data and combines it with multi-view support vector machine to construct a classification model for computer-aided diagnosis of autism. Firstly, a multi-output TSK fuzzy system is adopted to map the original feature data to the linear separable high-dimensional space. Then a manifold regularization learning framework is introduced, and a new method of unsupervised fuzzy feature learning is proposed. Finally, a multi-view SVM algorithm is used for classification tasks. The experimental results show that the method in this paper can effectively extract important features from the fMRI data in the resting state and improve the model's interpretability on the premise of ensuring the superior and stable classification performance of the model.
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Salunkhe, Sumit, Mrinal Bachute, Shilpa Gite, Nishad Vyas, Saanil Khanna, Keta Modi, Chinmay Katpatal, and Ketan Kotecha. "Classification of Alzheimer’s Disease Patients Using Texture Analysis and Machine Learning." Applied System Innovation 4, no. 3 (August 4, 2021): 49. http://dx.doi.org/10.3390/asi4030049.
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Alzheimer’s disease (AD) has been studied extensively to understand the nature of this complex disease and address the many research gaps concerning prognosis and diagnosis. Several studies based on structural and textural characteristics have already been conducted to aid in identifying AD patients. In this work, an image processing methodology was used to extract textural information and classify the patients into two groups: AD and Cognitively Normal (CN). The Gray Level Co-occurrence Matrix (GLCM) was employed since it is a strong foundation for texture classification. Various textural parameters derived from the GLCM aided in deciphering the characteristics of a Magnetic Resonance Imaging (MRI) region of interest (ROI). Several commonly used image classification algorithms were employed. MATLAB was used to successfully derive 20 features based on the GLCM of the MRI dataset. Based on the data analysis, 8 of the 20 features were determined as significant elements. Ensemble (90.2%), Decision Trees (88.5%), and Support Vector Machine (SVM) (87.2%) were the best performing classifiers. It was observed in GLCM that as the distance (d) between pixels increased, the classification accuracy decreased. The best result was observed for GLCM with d = 1 and direction (d, d, −d) with age and structural data.
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Roettger, Diana, Loizos Siakallis, Carole Sudre, Jasmina Panovska-Griffiths, Paul Mulholland, Lewis Thorne, Faiq Shaikh, and Sotirios Bisdas. "Combined structural and perfusion MRI enhanced by machine learning may outperform standalone modalities and radiological expertise in high-grade glioma surveillance: A proof-of-concept study." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e14528-e14528. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e14528.
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e14528 Background: Treatment monitoring in patients with High-Grade Glioma (HGG) and identification of disease progression, remains a major challenge in clinical neurooncology. We aimed to develop a support vector machine (SVM) classifier utilising combined longitudinal conventional and Dynamic Susceptibility Contrast (DSC) perfusion MRI to classify between Stable Disease (SD), Pseudoprogression (PsP) and Progressive Disease (PD) in glioma patients under surveillance. Methods: Conventional (269) and perfusion (62) MRI studies of HGG patients acquired between 2012 and 2018 were prospectively analysed. Study participants were separated into two groups: Group I with a single DSC time point (64 participants) and Group II with multiple DSC time points (19 participants). The SVM classifier was trained using all available MRI for each group. Classification accuracy was assessed for the use of features extracted from different feature dataset and time point combinations and compared to the experienced radiologists’ predictions. Results: The study included 64 participants (mean age: 48.5 ± 12.8 yrs [standard deviation], 24 female). SVM classification based on combined perfusion and structural features outperformed standalone datasets across all groups. For the clinically relevant classification step (SD/PSP vs PD), both feature combination as well as the addition of multiple DSC time points, improved classification performance (lowest median error rate: 0.016). The SVM algorithm outperformed radiologists in predicting lesion destiny in both groups. Optimal performance was observed in Group II, in which SVM sensitivity/specificity/accuracy was 100/91.67/94.7% for analysis based on the first time point and 85.71/100/ 94.7% based on multiple time points compared to 60/78/68% and 70/90/84.2% for the respective radiologist classifications. In Group I, the SVM also exceeded radiologist predictions, albeit by a smaller margin and resulted in sensitivity/specificity of 86.49/75.00/81.53% (SVM) compared to 75.7/68.9/73.84% (radiologists). Conclusions: Our results indicate that the addition of multiple longitudinal perfusion time points as well as the combination of structural and perfusion features significantly enhance classification outcome in treatment monitoring of HGGs and machine-learning-assisted diagnosis has potentially superior accuracy than the radiologist's visual evaluation and expertise.
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Rasool, Mohammed, Nor Azman Ismail, Wadii Boulila, Adel Ammar, Hussein Samma, Wael M. S. Yafooz, and Abdel-Hamid M. Emara. "A Hybrid Deep Learning Model for Brain Tumour Classification." Entropy 24, no. 6 (June 8, 2022): 799. http://dx.doi.org/10.3390/e24060799.
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A brain tumour is one of the major reasons for death in humans, and it is the tenth most common type of tumour that affects people of all ages. However, if detected early, it is one of the most treatable types of tumours. Brain tumours are classified using biopsy, which is not usually performed before definitive brain surgery. An image classification technique for tumour diseases is important for accelerating the treatment process and avoiding surgery and errors from manual diagnosis by radiologists. The advancement of technology and machine learning (ML) can assist radiologists in tumour diagnostics using magnetic resonance imaging (MRI) images without invasive procedures. This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images. The method suggested in this paper uses hybrid deep learning classification based on CNN with two methods. The first method combines a pre-trained Google-Net model of the CNN algorithm for feature extraction with SVM for pattern classification. The second method integrates a finely tuned Google-Net with a soft-max classifier. The proposed approach was evaluated using MRI brain images that contain a total of 1426 glioma images, 708 meningioma images, 930 pituitary tumour images, and 396 normal brain images. The reported results showed that an accuracy of 93.1% was achieved from the finely tuned Google-Net model. However, the synergy of Google-Net as a feature extractor with an SVM classifier improved recognition accuracy to 98.1%.
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Lama, Ramesh Kumar, Jeonghwan Gwak, Jeong-Seon Park, and Sang-Woong Lee. "Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features." Journal of Healthcare Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/5485080.
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Alzheimer’s disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR) images to discriminate AD, mild cognitive impairment (MCI), and healthy control (HC) subjects using a support vector machine (SVM), an import vector machine (IVM), and a regularized extreme learning machine (RELM). The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer’s disease neuroimaging initiative (ADNI) datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.
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Astolfi, Rodrigo S., Daniel S. da Silva, Ingrid S. Guedes, Caio S. Nascimento, Robertas Damaševičius, Senthil K. Jagatheesaperumal, Victor Hugo C. de Albuquerque, and José Alberto D. Leite. "Computer-Aided Ankle Ligament Injury Diagnosis from Magnetic Resonance Images Using Machine Learning Techniques." Sensors 23, no. 3 (February 1, 2023): 1565. http://dx.doi.org/10.3390/s23031565.
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Ankle injuries caused by the Anterior Talofibular Ligament (ATFL) are the most common type of injury. Thus, finding new ways to analyze these injuries through novel technologies is critical for assisting medical diagnosis and, as a result, reducing the subjectivity of this process. As a result, the purpose of this study is to compare the ability of specialists to diagnose lateral tibial tuberosity advancement (LTTA) injury using computer vision analysis on magnetic resonance imaging (MRI). The experiments were carried out on a database obtained from the Vue PACS–Carestream software, which contained 132 images of ATFL and normal (healthy) ankles. Because there were only a few images, image augmentation techniques was used to increase the number of images in the database. Following that, various feature extraction algorithms (GLCM, LBP, and HU invariant moments) and classifiers such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were used. Based on the results from this analysis, for cases that lack clear morphologies, the method delivers a hit rate of 85.03% with an increase of 22% over the human expert-based analysis.
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Hussain, Lal, Sharjil Saeed, Imtiaz Ahmed Awan, Adnan Idris, Malik Sajjad Ahmed Nadeem, and Qurat-ul-Ain Chaudhry. "Detecting Brain Tumor using Machines Learning Techniques Based on Different Features Extracting Strategies." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 6 (July 5, 2019): 595–606. http://dx.doi.org/10.2174/1573405614666180718123533.
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Background: Brain tumor is the leading cause of death worldwide. It is obvious that the chances of survival can be increased if the tumor is identified and properly classified at an initial stage. MRI (Magnetic Resonance Imaging) is one source of brain tumors detection tool and is extensively used in the diagnosis of brain to detect blood clots. In the past, many researchers developed Computer-Aided Diagnosis (CAD) systems that help the radiologist to detect the abnormalities in an efficient manner. Objective: The aim of this research is to improve the brain tumor detection performance by proposing a multimodal feature extracting strategy and employing machine learning techniques. Methods: In this study, we extracted multimodal features such as texture, morphological, entropybased, Scale Invariant Feature Transform (SIFT), and Elliptic Fourier Descriptors (EFDs) from brain tumor imaging database. The tumor was detected using robust machine learning techniques such as Support Vector Machine (SVM) with kernels: polynomial, Radial Base Function (RBF), Gaussian; Decision Tree (DT), and Naïve Bayes. Most commonly used Jack-knife 10-fold Cross- Validation (CV) was used for testing and validation of dataset. Results: The performance was evaluated in terms of specificity, sensitivity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), Total Accuracy (TA), Area under the receiver operating Curve (AUC), and P-value. The highest performance of 100% in terms of Specificity, Sensitivity, PPV, NPV, TA, AUC using Naïve Bayes classifiers based on entropy, morphological, SIFT and texture features followed by Decision Tree classifier with texture features (TA=97.81%, AUC=1.0) and SVM polynomial kernel with texture features (TA=94.63%). The highest significant p-value was obtained using SVM polynomial with texture features (P-value 2.65e-104) followed by SVM RB with texture features (P-value 1.96e-98). Conclusion: The results reveal that Naïve Bayes followed by Decision Tree gives highest detection accuracy based on entropy, morphological, SIFT and texture features.
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Khedher, Laila, Ignacio A. Illán, Juan M. Górriz, Javier Ramírez, Abdelbasset Brahim, and Anke Meyer-Baese. "Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer’s with Visual Support." International Journal of Neural Systems 27, no. 03 (February 27, 2017): 1650050. http://dx.doi.org/10.1142/s0129065716500507.
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Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer’s disease (AD), but limitations on interpretability and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer’s disease neuroimaging initiative (ADNI) participants for automatic classification. The proposed CAD system possesses two relevant characteristics: optimal performance and visual support for decision making. The CAD is built in two stages: a first feature extraction based on independent component analysis (ICA) on class mean images and, secondly, a support vector machine (SVM) training and classification. The obtained features for classification offer a full graphical representation of the images, giving an understandable logic in the CAD output, that can increase confidence in the CAD support. The proposed method yields classification results up to 89% of accuracy (with 92% of sensitivity and 86% of specificity) for normal controls (NC) and AD patients, 79% of accuracy (with 82% of sensitivity and 76% of specificity) for NC and mild cognitive impairment (MCI), and 85% of accuracy (with 85% of sensitivity and 86% of specificity) for MCI and AD patients.
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Battineni, Gopi, Nalini Chintalapudi, Francesco Amenta, and Enea Traini. "A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects." Journal of Clinical Medicine 9, no. 7 (July 8, 2020): 2146. http://dx.doi.org/10.3390/jcm9072146.
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Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic feature selection was conducted by wrapping methods, and the NB achieved the highest ROC of 0.942. The last experiment consisted of an ensemble or hybrid modeling developed to combine the four models. This approach resulted in an improved accuracy ROC of 0.991. We conclude that the involvement of ensemble modeling, coupled with selective features, can predict with better accuracy the development of AD at an early stage.
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Sangeetha, G., and G. Vadivu. "Analysis of Deep Learning Techniques for Brain Tumour Classification from CT & MRI Images." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 12 (December 31, 2022): 211–18. http://dx.doi.org/10.17762/ijritcc.v10i12.5944.
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Brain tumour detection in an initialpoint is a critical step to saving human life. Computed Tomography (CT) and Magnetic Resonance Image (MRI) provide very detailed information about brain tumour tissues. So the segmentation of tumour region is possible from pancreatic CT and brain MRI. CT and MRI is a non-invasive technique and it does not produce any harmful radiation to the patient. The patient suspected of tumour undergoes radiological evaluation such that the area, location and grade of the tumour can be predicted from the CT and MRI analysis. This critical information helps the doctors to decide about further treatment like chemotherapy, surgery, or radiation. The diagnosis requires an accurate and very fast segmentation and classification of CT and MRI images. But nowadays radiologists are doing this task manually and it is a tedious and time-consuming procedure. Also, there is a chance of variation in the result from one expert to another. Here comes the significance of automatic segmentation and classification of tumour types with the help of computers.The proposed work aims to develop an efficient system that can detect pancreatic and brain tumour and can classify the pancreatic CT and brain MRI into normal, benign or malignant. This work can be categorized into two approaches. Thus the dataset prepared for this research work contains CT and MRI images.The first approach proposes traditional machine learning technologies to achieve the goal. Image pre-processing, feature extraction, segmentation and classification are the various steps of the traditional machine learning method. A detailed investigation is performed through various feature extraction techniques and classification techniques for pancreatic (CT) and brain MRI. Discrete Wavelet Transform (DWT) feature, Grey Level Co-occurrence Matrix (GLCM) feature, Gabor feature, Tamura features and Edge Orientation Histogram (EOH) features and their combinations are used for the extraction of CT and MRI features. Benign tumours are non-cancerous, but malignant tumours are cancerous. In the first approach, the Support Vector Machine (SVM) is the main classifier used for pancreatic CT and brain MRI classification as normal, benign or malignant.In this technology, a huge amount of data and machines with high computational capabilities like Graphic Processing Unit (GPU) are available. Thus the second approach of this paper is to exploit all these available resources to produce accurate results. In this part, deep learning, the latest fast growing technology introduced in 2015 is used for the classification of brain MRI. A Deep Convolutional Neural Networks (DCNN) model is proposed to perform the classification task efficiently. The CNN results are compared with the results of a simple neural network classifier. This method provides accurate and it shows that deep learning based classification outperforms traditional machine learning techniques which produce an accurate result only. This research work again concentrates on the Transfer Learning (TL) methods to classify pancreatic CT and brain MRI.
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Uzun Ozsahin, Dilber, Efe Precious Onakpojeruo, Berna Uzun, Mubarak Taiwo Mustapha, and Ilker Ozsahin. "Mathematical Assessment of Machine Learning Models Used for Brain Tumor Diagnosis." Diagnostics 13, no. 4 (February 8, 2023): 618. http://dx.doi.org/10.3390/diagnostics13040618.
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The brain is an intrinsic and complicated component of human anatomy. It is a collection of connective tissues and nerve cells that regulate the principal actions of the entire body. Brain tumor cancer is a serious mortality factor and a highly intractable disease. Even though brain tumors are not considered a fundamental cause of cancer deaths worldwide, about 40% of other cancer types are metastasized to the brain and transform into brain tumors. Computer-aided devices for diagnosis through magnetic resonance imaging (MRI) have remained the gold standard for the diagnosis of brain tumors, but this conventional method has been greatly challenged with inefficiencies and drawbacks related to the late detection of brain tumors, high risk in biopsy procedures, and low specificity. To circumvent these underlying hurdles, machine learning models have recently been developed to enhance computer-aided diagnosis tools for advanced, precise, and automatic early detection of brain tumors. This study takes a novel approach to evaluate machine learning models (support vector machine (SVM), random forest (RF), gradient-boosting model (GBM), convolutional neural network (CNN), K-nearest neighbor (KNN), AlexNet, GoogLeNet, CNN VGG19, and CapsNet) used for the early detection and classification of brain tumors by deploying the multicriteria decision-making method called fuzzy preference ranking organization method for enrichment evaluations (PROMETHEE), based on selected parameters, in this study: prediction accuracy, precision, specificity, recall, processing time, and sensitivity. To validate the results of our proposed approach, we performed a sensitivity analysis and cross-checking analysis with the PROMETHEE model. The CNN model, with an outranking net flow of 0.0251, is considered the most favorable model for the early detection of brain tumors. The KNN model, with a net flow of −0.0154, is the least appealing option. The findings of this study support the applicability of the proposed approach for making optimal choices regarding the selection of machine learning models. The decision maker is thus afforded the opportunity to expand the range of considerations which they must rely on in selecting the preferred models for early detection of brain tumors.
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Mikolas, P., T. Melicher, A. Skoch, M. Matejka, A. Slovakova, E. Bakstein, T. Hajek, and F. Spaniel. "Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: a machine-learning study." Psychological Medicine 46, no. 13 (July 25, 2016): 2695–704. http://dx.doi.org/10.1017/s0033291716000878.
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BackgroundEarly diagnosis of schizophrenia could improve the outcomes and limit the negative effects of untreated illness. Although participants with schizophrenia show aberrant functional connectivity in brain networks, these between-group differences have a limited diagnostic utility. Novel methods of magnetic resonance imaging (MRI) analyses, such as machine learning (ML), may help bring neuroimaging from the bench to the bedside. Here, we used ML to differentiate participants with a first episode of schizophrenia-spectrum disorder (FES) from healthy controls based on resting-state functional connectivity (rsFC).MethodWe acquired resting-state functional MRI data from 63 patients with FES who were individually matched by age and sex to 63 healthy controls. We applied linear kernel support vector machines (SVM) to rsFC within the default mode network, the salience network and the central executive network.ResultsThe SVM applied to the rsFC within the salience network distinguished the FES from the control participants with an accuracy of 73.0% (p = 0.001), specificity of 71.4% and sensitivity of 74.6%. The classification accuracy was not significantly affected by medication dose, or by the presence of psychotic symptoms. The functional connectivity within the default mode or the central executive networks did not yield classification accuracies above chance level.ConclusionsSeed-based functional connectivity maps can be utilized for diagnostic classification, even early in the course of schizophrenia. The classification was probably based on trait rather than state markers, as symptoms or medications were not significantly associated with classification accuracy. Our results support the role of the anterior insula/salience network in the pathophysiology of FES.
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Trigui, Rania, Mouloud Adel, Mathieu Di Bisceglie, Julien Wojak, Jessica Pinol, Alice Faure, and Kathia Chaumoitre. "Bladder Wall Segmentation and Characterization on MR Images: Computer-Aided Spina Bifida Diagnosis." Journal of Imaging 8, no. 6 (May 25, 2022): 151. http://dx.doi.org/10.3390/jimaging8060151.
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(1) Background: Segmentation of the bladder inner’s wall and outer boundaries on Magnetic Resonance Images (MRI) is a crucial step for the diagnosis and the characterization of the bladder state and function. This paper proposes an optimized system for the segmentation and the classification of the bladder wall. (2) Methods: For each image of our data set, the region of interest corresponding to the bladder wall was extracted using LevelSet contour-based segmentation. Several features were computed from the extracted wall on T2 MRI images. After an automatic selection of the sub-vector containing most discriminant features, two supervised learning algorithms were tested using a bio-inspired optimization algorithm. (3) Results: The proposed system based on the improved LevelSet algorithm proved its efficiency in bladder wall segmentation. Experiments also showed that Support Vector Machine (SVM) classifier, optimized by Gray Wolf Optimizer (GWO) and using Radial Basis Function (RBF) kernel outperforms the Random Forest classification algorithm with a set of selected features. (4) Conclusions: A computer-aided optimized system based on segmentation and characterization, of bladder wall on MRI images for classification purposes is proposed. It can significantly be helpful for radiologists as a part of spina bifida study.
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Khatri, Uttam, and Goo-Rak Kwon. "An Efficient Combination among sMRI, CSF, Cognitive Score, and APOE ε4 Biomarkers for Classification of AD and MCI Using Extreme Learning Machine." Computational Intelligence and Neuroscience 2020 (June 4, 2020): 1–18. http://dx.doi.org/10.1155/2020/8015156.
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Alzheimer’s disease (AD) is the most common cause of dementia and a progressive neurodegenerative condition, characterized by a decline in cognitive function. Symptoms usually appear gradually and worsen over time, becoming severe enough to interfere with individual daily tasks. Thus, the accurate diagnosis of both AD and the prodromal stage (i.e., mild cognitive impairment (MCI)) is crucial for timely treatment. As AD is inherently dynamic, the relationship between AD indicators is unclear and varies over time. To address this issue, we first aimed at investigating differences in atrophic patterns between individuals with AD and MCI and healthy controls (HCs). Then we utilized multiple biomarkers, along with filter- and wrapper-based feature selection and an extreme learning machine- (ELM-) based approach, with 10-fold cross-validation for classification. Increasing efforts are focusing on the use of multiple biomarkers, which can be useful for the diagnosis of AD and MCI. However, optimum combinations have yet to be identified and most multimodal analyses use only volumetric measures obtained from magnetic resonance imaging (MRI). Anatomical structural MRI (sMRI) measures have also so far mostly been used separately. The full possibilities of using anatomical MRI for AD detection have thus yet to be explored. In this study, three measures (cortical thickness, surface area, and gray matter volume), obtained from sMRI through preprocessing for brain atrophy measurements; cerebrospinal fluid (CSF), for quantification of specific proteins; cognitive score, as a measure of cognitive performance; and APOE ε4 allele status were utilized. Our results show that a combination of specific biomarkers performs well, with accuracies of 97.31% for classifying AD vs. HC, 91.72% for MCI vs. HC, 87.91% for MCI vs. AD, and 83.38% for MCIs vs. MCIc, respectively, when evaluated using the proposed algorithm. Meanwhile, the areas under the curve (AUC) from the receiver operating characteristic (ROC) curves combining multiple biomarkers provided better classification performance. The proposed features combination and selection algorithm effectively classified AD and MCI, and MCIs vs. MCIc, the most challenging classification task, and therefore could increase the accuracy of AD classification in clinical practice. Furthermore, we compared the performance of the proposed method with SVM classifiers, using a cross-validation method with Alzheimer’s Disease Neuroimaging Initiative (ADNI) datasets.
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Romeo, Valeria, Panagiotis Kapetas, Paola Clauser, Pascal A. T. Baltzer, Sazan Rasul, Peter Gibbs, Marcus Hacker, Ramona Woitek, Katja Pinker, and Thomas H. Helbich. "A Simultaneous Multiparametric 18F-FDG PET/MRI Radiomics Model for the Diagnosis of Triple Negative Breast Cancer." Cancers 14, no. 16 (August 16, 2022): 3944. http://dx.doi.org/10.3390/cancers14163944.
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Purpose: To investigate whether a machine learning (ML)-based radiomics model applied to 18F-FDG PET/MRI is effective in molecular subtyping of breast cancer (BC) and specifically in discriminating triple negative (TN) from other molecular subtypes of BC. Methods: Eighty-six patients with 98 BC lesions (Luminal A = 10, Luminal B = 51, HER2+ = 12, TN = 25) were included and underwent simultaneous 18F-FDG PET/MRI of the breast. A 3D segmentation of BC lesion was performed on T2w, DCE, DWI and PET images. Quantitative diffusion and metabolic parameters were calculated and radiomics features extracted. Data were selected using the LASSO regression and used by a fine gaussian support vector machine (SVM) classifier with a 5-fold cross validation for identification of TNBC lesions. Results: Eight radiomics models were built based on different combinations of quantitative parameters and/or radiomic features. The best performance (AUROC 0.887, accuracy 82.8%, sensitivity 79.7%, specificity 86%, PPV 85.3%, NPV 80.8%) was found for the model combining first order, neighborhood gray level dependence matrix and size zone matrix-based radiomics features extracted from ADC and PET images. Conclusion: A ML-based radiomics model applied to 18F-FDG PET/MRI is able to non-invasively discriminate TNBC lesions from other BC molecular subtypes with high accuracy. In a future perspective, a “virtual biopsy” might be performed with radiomics signatures.
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Peng, Tao, JianMing Xiao, Lin Li, BingJie Pu, XiangKe Niu, XiaoHui Zeng, ZongYong Wang, et al. "Can machine learning-based analysis of multiparameter MRI and clinical parameters improve the performance of clinically significant prostate cancer diagnosis?" International Journal of Computer Assisted Radiology and Surgery 16, no. 12 (October 22, 2021): 2235–49. http://dx.doi.org/10.1007/s11548-021-02507-w.
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Abstract Purpose To establish machine learning(ML) models for the diagnosis of clinically significant prostate cancer (csPC) using multiparameter magnetic resonance imaging (mpMRI), texture analysis (TA), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative analysis and clinical parameters and to evaluate the stability of these models in internal and temporal validation. Methods The dataset of 194 men was split into training (n = 135) and internal validation (n = 59) cohorts, and a temporal dataset (n = 58) was used for evaluation. The lesions with Gleason score ≥ 7 were defined as csPC. Logistic regression (LR), stepwise regression (SR), classical decision tree (cDT), conditional inference tree (CIT), random forest (RF) and support vector machine (SVM) models were established by combining mpMRI-TA, DCE-MRI and clinical parameters and validated by internal and temporal validation using the receiver operating characteristic (ROC) curve and Delong’s method. Results Eight variables were determined as important predictors for csPC, with the first three related to texture features derived from the apparent diffusion coefficient (ADC) mapping. RF, LR and SR models yielded larger and more stable area under the ROC curve values (AUCs) than other models. In the temporal validation, the sensitivity was lower than that of the internal validation (p < 0.05). There were no significant differences in specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV) and AUC (p > 0.05). Conclusions Each machine learning model in this study has good classification ability for csPC. Compared with internal validation, the sensitivity of each machine learning model in temporal validation was reduced, but the specificity, accuracy, PPV, NPV and AUCs remained stable at a good level. The RF, LR and SR models have better classification performance in the imaging-based diagnosis of csPC, and ADC texture-related parameters are of the highest importance.
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L, Reshma, Sai Priya Nalluri, and Priya R. Sankpal. "A Deep Learning-Based Approach for Detection of Dementia from Brain Mri." Journal of University of Shanghai for Science and Technology 23, no. 07 (July 9, 2021): 516–29. http://dx.doi.org/10.51201/jusst/21/07177.
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In this paper, a user-friendly system has been developed which will provide the result of medical analysis of digital images like magnetization resonance of image scan of the brain for detection and classification of dementia. The small structural differences in the brain can slowly and gradually become a major disease like dementia. The progression of dementia can be slowed when identified early. Hence, this paper aims at developing a robust system for classification and identifying dementia at the earliest. The method used in this paper for initial disclosure and diagnosis of dementia is deep learning since it can give important results in a shorter period of time. Deep Learning methods such as K-means clustering, Pattern Recognition, and Multi-class Support Vector Machine (SVM) have been used to classify different stages of dementia. The goal of this study is to provide a user interface for deep learning-based dementia classification using brain magnetic resonance imaging data. The results show that the created method has an accuracy of 96% and may be utilized to detect people who have dementia or are in the early stages of dementia.
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Xu, Zelin, Hongmin Deng, Jin Liu, and Yang Yang. "Diagnosis of Alzheimer’s Disease Based on the Modified Tresnet." Electronics 10, no. 16 (August 9, 2021): 1908. http://dx.doi.org/10.3390/electronics10161908.
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In the medical field, Alzheimer’s disease (AD), as a neurodegenerative brain disease which is very difficult to diagnose, can cause cognitive impairment and memory decline. Many existing works include a variety of clinical neurological and psychological examinations, especially computer-aided diagnosis (CAD) methods based on electroencephalographic (EEG) recording or MRI images by using machine learning (ML) combined with different preprocessing steps such as hippocampus shape analysis, fusion of embedded features, and so on, where EEG dataset used for AD diagnosis is usually is large and complex, requiring extraction of a series of features like entropy features, spectral feature, etc., and it has seldom been applied in the AD detection based on deep learning (DL), while MRI images were suitable for both ML and DL. In terms of the structural MRI brain images, few differences could be found in brain atrophy among the three situations: AD, mild cognitive impairment (MCI), and Normal Control (NC). On the other hand, DL methods have been used to diagnose AD incorporating MRI images in recent years, but there have not yet been many selective models with very deep layers. In this article, the Gray Matter (GM) Magnetic Resonance Imaging (MRI) is automatically extracted, which could better distinguish among the three types of situations like AD, MCI, and NC, compared with Cerebro Spinal Fluid (CSF) and White Matter (WM). Firstly, FMRIB Software Library (FSL) software is utilized for batch processing to remove the skull, cerebellum and register the heterogeneous images, and the SPM + cat12 tool kits in MATLAB is used to segment MRI images for obtaining the standard GM MRI images. Next, the GM MRI images are trained by some new neural networks. The characteristics of the training process are as follows: (1) The Tresnet, as the network that achieves the best classification effect among several new networks in the experiment, is selected as the basic network. (2) A multi-receptive-field mechanism is integrated into the network, which is inspired by neurons that can dynamically adjust the receptive fields according to different stimuli. (3) The whole network is realized by adding multiple channels to the convolutional layer, and the size of the convolution kernel of each channel can be dynamically adjusted. (4) Transfer learning method is used to train the model for speeding up the learning and optimizing the learning efficiency. Finally, we achieve the accuracies of 86.9% for AD vs. NC, 63.2% for AD vs. MCI vs. NC respectively, which outperform the previous approaches. The results demonstrate the effectiveness of our approach.
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Kuraparthi, Swaraja, Madhavi K. Reddy, C. N. Sujatha, Himabindu Valiveti, Chaitanya Duggineni, Meenakshi Kollati, Padmavathi Kora, and V. Sravan. "Brain Tumor Classification of MRI Images Using Deep Convolutional Neural Network." Traitement du Signal 38, no. 4 (August 31, 2021): 1171–79. http://dx.doi.org/10.18280/ts.380428.
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Manual tumor diagnosis from magnetic resonance images (MRIs) is a time-consuming procedure that may lead to human errors and may lead to false detection and classification of the tumor type. Therefore, to automatize the complex medical processes, a deep learning framework is proposed for brain tumor classification to ease the task of doctors for medical diagnosis. Publicly available datasets such as Kaggle and Brats are used for the analysis of brain images. The proposed model is implemented on three pre-trained Deep Convolution Neural Network architectures (DCNN) such as AlexNet, VGG16, and ResNet50. These architectures are the transfer learning methods used to extract the features from the pre-trained DCNN architecture, and the extracted features are classified by using the Support Vector Machine (SVM) classifier. Data augmentation methods are applied on Magnetic Resonance images (MRI) to avoid the network from overfitting. The proposed methodology achieves an overall accuracy of 98.28% and 97.87% without data augmentation and 99.0% and 98.86% with data augmentation for Kaggle and Brat's datasets, respectively. The Area Under Curve (AUC) for Receiver Operator Characteristic (ROC) is 0.9978 and 0.9850 for the same datasets. The result shows that ResNet50 performs best in the classification of brain tumors when compared with the other two networks.
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Ahmadi, Mohsen, Fatemeh Dashti Ahangar, Nikoo Astaraki, Mohammad Abbasi, and Behzad Babaei. "FWNNet: Presentation of a New Classifier of Brain Tumor Diagnosis Based on Fuzzy Logic and the Wavelet-Based Neural Network Using Machine-Learning Methods." Computational Intelligence and Neuroscience 2021 (November 22, 2021): 1–13. http://dx.doi.org/10.1155/2021/8542637.
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In this paper, we present a novel classifier based on fuzzy logic and wavelet transformation in the form of a neural network. This classifier includes a layer to predict the numerical feature corresponded to labels or classes. The presented classifier is implemented in brain tumor diagnosis. For feature extraction, a fractal model with four Gaussian functions is used. The classification is performed on 2000 MRI images. Regarding the results, the accuracy of the DT, KNN, LDA, NB, MLP, and SVM is 93.5%, 87.6%, 61.5%, 57.5%, 68.5%, and 43.6%, respectively. Based on the results, the presented FWNNet illustrates the highest accuracy of 100% with the fractal feature extraction method and brain tumor diagnosis based on MRI images. Based on the results, the best classifier for diagnosis of the brain tumor is FWNNet architecture. However, the second and third high-performance classifiers are the DT and KNN, respectively. Moreover, the presented FWNNet method is implemented for the segmentation of brain tumors. In this paper, we present a novel supervised segmentation method based on the FWNNet layer. In the training process, input images with a sweeping filter should be reshaped to vectors that correspond to reshaped ground truth images. In the training process, we performed a PSO algorithm to optimize the gradient descent algorithm. For this purpose, 80 MRI images are used to segment the brain tumor. Based on the results of the ROC curve, it can be estimated that the presented layer can segment the brain tumor with a high true-positive rate.
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Pitchai, R., Bhasker Dappuri, P. V. Pramila, M. Vidhyalakshmi, S. Shanthi, Wadi B. Alonazi, Khalid M. A. Almutairi, R. S. Sundaram, and Ibsa Beyene. "An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning Approach." Computational Intelligence and Neuroscience 2022 (October 12, 2022): 1–9. http://dx.doi.org/10.1155/2022/5489084.
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Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals. Patients will be able to obtain prompt therapy in this manner if they are carefully monitored; their biosignals will be precisely assessed and real-time analysis will be performed. On the contrary, most stroke diagnosis and prediction systems rely on image analysis technologies such as CT or MRI, which are not only expensive but also hard to use. In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. To improve the accuracy of prediction, the samples are generated using the data augmentation principle, which supports training with vast data. The simulation is conducted to test the efficacy of the model, and the results show that the proposed classifier achieves a higher rate of classification accuracy than the existing methods. Furthermore, it is seen that the rate of precision, recall, and f-measure is higher in the proposed SVM than in other methods.
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Sodhi, Alisha, Isha Wadhavkar, Kartik Varadarajan, Orhun Muratoglu, Alireza Borjali, and Miho Tanaka. "Poster 221: Application of Machine Learning to Diagnose Patellar Instability on MRI Using a Data-Driven Model." Orthopaedic Journal of Sports Medicine 10, no. 7_suppl5 (July 1, 2022): 2325967121S0078. http://dx.doi.org/10.1177/2325967121s00782.
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Objectives: Patellar instability has multiple anatomic risk factors including trochlear dysplasia, patella alta and tuberosity lateralization. However, the exact contribution of each factor on patellar stability has not been clearly delineated due to the fact that 1) each abnormality exists along a spectrum that varies between individuals, 2) multiple measurements exist to describe each risk factor, and 3) the additive effects of these morphological abnormalities are not yet understood. The advent of modern machine learning techniques allows us the opportunity to extract predictive insights by detecting complex patterns underlying health data. We aimed to apply machine learning to differentiate between knee MRI measurements of patients with and without patellar instability to determine the complex relationships across variables that contribute to patellar instability. Methods: Utilizing an institutional database, knee MRIs of patients between the ages 18 to 40 at the time of imaging with a diagnosis of recurrent patellar instability were identified. Age and sex matched controls were selected from a database of MRIs with a diagnosis of meniscal tear for comparison. 26 standard measurements that have been used to describe trochlear dysplasia, patella alta, and tuberosity lateralization were performed on each knee, including bony and cartilaginous landmarks for each measurement when applicable. Using these measurements, 3 categories of machine learning methods were performed, including interpretable model-based methods (linear model, decision tree, random forest, and gradient boosted tree), non-interpretable model-based methods (neural network, and support vector machine [SVM]), and instance-based method (k-nearest neighbor [KNN]). A split validation method was used to divide the entire dataset into train, validation, and test subsets with an 80:10:10 split-ratio. Results: 130 knees with patellar instability (47M, 83F) were included in this study and compared with 130 age- and sex-matched control knees. Table 1 summarizes the performance of each method on the test subset. The decision tree achieved the highest overall accuracy among the interpretable model-based methods, followed by SVM, and KNN among non-interpretable model-based and instance-based methods, respectively. Figure 1 depicts the decision tree model, demonstrating that bisect offsect measurement >72.1%, followed by length of patello-trochlear overlap <= 1.35mm, and cartilaginous trochlear depth <= 3.18mm predicted the presence of patellar instability with an accuracy of 84.62%. Conclusions: We present a novel approach to identify patellar instability from MRI measurements, in which an interpretable model-based machine learning method (decision tree) achieved the highest overall performance. Furthermore, patellar lateralization as reflected by bisect offset, patella alta as reflected by patellotrochlear overlap, and cartilaginous trochlear depth indicating dysplasia were found to identify knees with patellar instability. Application of this model and the utilization of uniform measurements can potentially improve the diagnostic accuracy of patellar instability from MRI images. Prospective clinical studies are recommended to further validate this model for its utility in the prediction of patellar instability. [Table: see text][Figure: see text]
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Samadi Ghoushchi, Hamed, and Yaghoub Pourasad. "Clustering of Brain Tumors in Brain MRI Images based on Extraction of Textural and Statistical Features." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 12 (October 19, 2020): 116. http://dx.doi.org/10.3991/ijoe.v16i12.16929.
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<p>The purpose of this article is to investigate techniques for classifying tumor grade from magnetic resonance imaging (MRI). This requires early diagnosis of the brain tumor and its grade. Magnetic resonance imaging may show a clear tumor in the brain, but doctors need to measure the tumor in order to treat more or to advance treatment. For this purpose, digital imaging techniques along with machine learning can help to quickly identify tumors and also treatments and types of surgery. These combined techniques in understanding medical images for researchers are an important tool to increase the accuracy of diagnosis. In this paper, classification methods for MRI images of tumors of the human brain are performed to review the astrocytoma-containing glands. Methods used to classify brain tumors, including preprocessing, screening, tissue extraction, and statistical features of the tumor using two types of T<sub>1</sub>W and Flair brain MRI images and also the method of dimensionality reduction of extracted features and how to train them in classification are also explained. Determine the tumor area using three classification of Fuzzy Logic <em>C</em><em>-</em><em>Means</em><em> </em>Clustering (FCM), Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM). In this paper, simulated and real MRI images are used. The results obtained from the proposed methods in this paper are compared with the reference results and the results show that the proposed approach can increase the reliability of brain tumor diagnosis.</p>