Relevant bibliographies by topics / Machine Learning, SVM, MRI, diagnosis

Academic literature on the topic 'Machine Learning, SVM, MRI, diagnosis'

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Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Machine Learning, SVM, MRI, diagnosis"

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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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Dissertations / Theses on the topic "Machine Learning, SVM, MRI, diagnosis"

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SALVATORE, CHRISTIAN. "Development and validation of a Decision Support System for the automatic diagnosis of medical images from brain MRI studies." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/94834.

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Decision Support Systems (DSS) for assisted medical diagnosis are computer-based systems designed to assist clinicians with decision-making tasks by automatically determining diagnosis or improving diagnostic confidence. This could allow to perform early and differential diagnosis of neurological diseases, such as Alzheimer’s Disease (AD) and Parkinson’s Disease (PD), for which definite diagnosis still remains a crucial issue. Multivariate Machine Learning (ML) methods are gaining popularity within the neuroimaging community. Among these, supervised ML methods are able to automatically extract multiple information from image sets without requiring prior knowledge of where information may be coded. These methods have been proposed as a revolutionary approach for identifying sensitive biomarkers allowing for automatic classification of individual subjects. The aim of this thesis was to implement, optimize and validate a ML method able to perform automatic diagnosis of medical images by structural Magnetic Resonance Imaging data (sMRI). This method consists of 3 phases: 1) image preprocessing, mainly devoted to the co-registration of data from different patients to a common reference system; 2) feature extraction and selection, performed through Principal Components Analysis and Fisher’s Discriminant Ratio, with the aim of extracting and selecting the most discriminative features; 3) classification, performed by Support Vector Machine, with the aim of computing a predictive model for the diagnosis of new subjects. Moreover, I implemented a method for the generation of pattern distribution maps of brain structural differences, reflecting the importance of each voxel for classification. These maps could allow to identify new MR-related biomarkers for the diagnosis of neurological diseases. In order to test the feasibility of the implemented method, I applied it to the diagnosis of 3 pathologies: AD, PD and Eating Disorders (ED). Regarding PD, we acquired T1-weighted brain sMRI of 28 PD, 28 PSP (Progressive Supranuclear Palsy) and 28 healthy controls (CN). Classification performance in terms of accuracy (specificity/sensitivity) (%) was 94(91/97) for PD vs CN, 92(93/92) for PSP vs CN, 92(91/94) for PSP vs PD. Voxels influencing differential diagnosis of PD were localized in midbrain, pons, corpus callosum and thalamus, four critical regions involved in the pathophysiological mechanisms of PD. Regarding AD, I enrolled 509 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, obtaining T1-weighted brain sMRI of 137 AD, 76 Mild Cognitive Impairment (MCI) patients who converted to AD (MCIc), 134 MCI who did not convert to AD (MCInc) within 18 months, and 162 CN. Classification performance (%) was 76±11 for AD vs CN, 72±12 for MCIc vs CN, 66±16 for MCIc vs MCInc. Voxels influencing the classification of AD vs CN were localized in the temporal pole, hippocampus, entorhinal cortex, amygdala, thalamus, putamen, caudate, insula, gyrus rectus, frontal and orbitofrontal cortices, anterior cingulate cortex, precuneus, posterior cerebellar lobule. Voxels influencing the classification of MCIc vs CN and MCIc vs MCInc were similar to those found for AD. Regarding ED, we acquired T1-weighted brain sMRI of 17 ED and 17 CN. The classifier allowed ED vs CN diagnosis with accuracy (specificity/sensitivity) of 85(73/93)%. Pattern distribution maps showed that voxels influencing ED vs CN discrimination were localized in the occipital cortex, posterior cerebellar lobule, precuneus, sensorimotor and premotor cortices, anterior cingulate and orbitofrontal cortices, all brain regions involved in the regulation of appetite and emotional processing. Results of this work were published in 7 ISI international papers, 3 indexed international papers, 1 international book chapter, 5 international conference proceedings and 1 national conference proceedings.
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Chen, Beichen, and Amy Jinxin Chen. "PCA based dimensionality reduction of MRI images for training support vector machine to aid diagnosis of bipolar disorder." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259621.

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This study aims to investigate how dimensionality reduction of neuroimaging data prior to training support vector machines (SVMs) affects the classification accuracy of bipolar disorder. This study uses principal component analysis (PCA) for dimensionality reduction. An open source data set of 19 bipolar and 31 control structural magnetic resonance imaging (sMRI) samples was used, part of the UCLA Consortium for Neuropsychiatric Phenomics LA5c Study funded by the NIH Roadmap Initiative aiming to foster breakthroughs in the development of novel treatments for neuropsychiatric disorders. The images underwent smoothing, feature extraction and PCA before they were used as input to train SVMs. 3-fold cross-validation was used to tune a number of hyperparameters for linear, radial, and polynomial kernels. Experiments were done to investigate the performance of SVM models trained using 1 to 29 principal components (PCs). Several PC sets reached 100% accuracy in the final evaluation, with the minimal set being the first two principal components. Accumulated variance explained by the PCs used did not have a correlation with the performance of the model. The choice of kernel and hyperparameters is of utmost importance as the performance obtained can vary greatly. The results support previous studies that SVM can be useful in aiding the diagnosis of bipolar disorder, and that the use of PCA as a dimensionality reduction method in combination with SVM may be appropriate for the classification of neuroimaging data for illnesses not limited to bipolar disorder. Due to the limitation of a small sample size, the results call for future research using larger collaborative data sets to validate the accuracies obtained.
Syftet med denna studie är att undersöka hur dimensionalitetsreduktion av neuroradiologisk data före träning av stödvektormaskiner (SVMs) påverkar klassificeringsnoggrannhet av bipolär sjukdom. Studien använder principalkomponentanalys (PCA) för dimensionalitetsreduktion. En datauppsättning av 19 bipolära och 31 friska magnetisk resonanstomografi(MRT) bilder användes, vilka tillhör den öppna datakällan från studien UCLA Consortium for Neuropsychiatric Phenomics LA5c som finansierades av NIH Roadmap Initiative i syfte att främja genombrott i utvecklingen av nya behandlingar för neuropsykiatriska funktionsnedsättningar. Bilderna genomgick oskärpa, särdragsextrahering och PCA innan de användes som indata för att träna SVMs. Med 3-delad korsvalidering inställdes ett antal parametrar för linjära, radiala och polynomiska kärnor. Experiment gjordes för att utforska prestationen av SVM-modeller tränade med 1 till 29 principalkomponenter (PCs). Flera PC uppsättningar uppnådde 100% noggrannhet i den slutliga utvärderingen, där den minsta uppsättningen var de två första PCs. Den ackumulativa variansen över antalet PCs som användes hade inte någon korrelation med prestationen på modellen. Valet av kärna och hyperparametrar är betydande eftersom prestationen kan variera mycket. Resultatet stödjer tidigare studier att SVM kan vara användbar som stöd för diagnostisering av bipolär sjukdom och användningen av PCA som en dimensionalitetsreduktionsmetod i kombination med SVM kan vara lämplig för klassificering av neuroradiologisk data för bipolär och andra sjukdomar. På grund av begränsningen med få dataprover, kräver resultaten framtida forskning med en större datauppsättning för att validera de erhållna noggrannheten.
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Cuingnet, Rémi. "Contributions à l’apprentissage automatique pour l’analyse d’images cérébrales anatomiques." Thesis, Paris 11, 2011. http://www.theses.fr/2011PA112033/document.

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L'analyse automatique de différences anatomiques en neuroimagerie a de nombreuses applications pour la compréhension et l'aide au diagnostic de pathologies neurologiques. Récemment, il y a eu un intérêt croissant pour les méthodes de classification telles que les machines à vecteurs supports pour dépasser les limites des méthodes univariées traditionnelles. Cette thèse a pour thème l'apprentissage automatique pour l'analyse de populations et la classification de patients en neuroimagerie. Nous avons tout d'abord comparé les performances de différentes stratégies de classification, dans le cadre de la maladie d'Alzheimer à partir d'images IRM anatomiques de 509 sujets de la base de données ADNI. Ces différentes stratégies prennent insuffisamment en compte la distribution spatiale des \textit{features}. C'est pourquoi nous proposons un cadre original de régularisation spatiale et anatomique des machines à vecteurs supports pour des données de neuroimagerie volumiques ou surfaciques, dans le formalisme de la régularisation laplacienne. Cette méthode a été appliquée à deux problématiques cliniques: la maladie d'Alzheimer et les accidents vasculaires cérébraux. L'évaluation montre que la méthode permet d'obtenir des résultats cohérents anatomiquement et donc plus facilement interprétables, tout en maintenant des taux de classification élevés
Brain image analyses have widely relied on univariate voxel-wise methods. In such analyses, brain images are first spatially registered to a common stereotaxic space, and then mass univariate statistical tests are performed in each voxel to detect significant group differences. However, the sensitivity of theses approaches is limited when the differences involve a combination of different brain structures. Recently, there has been a growing interest in support vector machines methods to overcome the limits of these analyses.This thesis focuses on machine learning methods for population analysis and patient classification in neuroimaging. We first evaluated the performances of different classification strategies for the identification of patients with Alzheimer's disease based on T1-weighted MRI of 509 subjects from the ADNI database. However, these methods do not take full advantage of the spatial distribution of the features. As a consequence, the optimal margin hyperplane is often scattered and lacks spatial coherence, making its anatomical interpretation difficult. Therefore, we introduced a framework to spatially regularize support vector machines for brain image analysis based on Laplacian regularization operators. The proposed framework was then applied to the analysis of stroke and of Alzheimer's disease. The results demonstrated that the proposed classifier generates less-noisy and consequently more interpretable feature maps with no loss of classification performance
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Abdulkadir, Ahmed [Verfasser], and Thomas [Akademischer Betreuer] Brox. "Brain MRI analysis and machine learning for diagnosis of neurodegeneration." Freiburg : Universität, 2018. http://d-nb.info/117696805X/34.

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Zarogianni, Eleni. "Machine learning and brain imaging in psychosis." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/22814.

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Over the past years early detection and intervention in schizophrenia have become a major objective in psychiatry. Early intervention strategies are intended to identify and treat psychosis prior to fulfilling diagnostic criteria for the disorder. To this aim, reliable early diagnostic biomarkers are needed in order to identify a high-risk state for psychosis and also predict transition to frank psychosis in those high-risk individuals destined to develop the disorder. Recently, machine learning methods have been successfully applied in the diagnostic classification of schizophrenia and in predicting transition to psychosis at an individual level based on magnetic resonance imaging (MRI) data and also neurocognitive variables. This work investigates the application of machine learning methods for the early identification of schizophrenia in subjects at high risk for developing the disorder. The dataset used in this work involves data from the Edinburgh High Risk Study (EHRS), which examined individuals at a heightened risk for developing schizophrenia for familial reasons, and the FePsy (Fruherkennung von Psychosen) study that was conducted in Basel and involves subjects at a clinical high-risk state for psychosis. The overriding aim of this thesis was to use machine learning, and specifically Support Vector Machine (SVM), in order to identify predictors of transition to psychosis in high-risk individuals, using baseline structural MRI data. There are three aims pertaining to this main one. (i) Firstly, our aim was to examine the feasibility of distinguishing at baseline those individuals who later developed schizophrenia from those who did not, yet had psychotic symptoms using SVM and baseline data from the EHRS study. (ii) Secondly, we intended to examine if our classification approach could generalize to clinical high-risk cohorts, using neuroanatomical data from the FePsy study. (iii) In a more exploratory context, we have also examined the diagnostic performance of our classifier by pooling the two datasets together. With regards to the first aim, our findings suggest that the early prediction of schizophrenia is feasible using a MRI-based linear SVM classifier operating at the single-subject level. Additionally, we have shown that the combination of baseline neuroanatomical data with measures of neurocognitive functioning and schizotypal cognition can improve predictive performance. The application of our pattern classification approach to baseline structural MRI data from the FePsy study highly replicated our previous findings. Our classification method identified spatially distributed networks that discriminate at baseline between subjects that later developed schizophrenia and other related psychoses and those that did not. Finally, a preliminary classification analysis using pooled datasets from the EHRS and the FePsy study supports the existence of a neuroanatomical pattern that differentiates between groups of high-risk subjects that develop psychosis against those who do not across research sites and despite any between-sites differences. Taken together, our findings suggest that machine learning is capable of distinguishing between cohorts of high risk subjects that later convert to psychosis and those that do not based on patterns of structural abnormalities that are present before disease onset. Our findings have some clinical implications in that machine learning-based approaches could advise or complement clinical decision-making in early intervention strategies in schizophrenia and related psychoses. Future work will be, however, required to tackle issues of reproducibility of early diagnostic biomarkers across research sites, where different assessment criteria and imaging equipment and protocols are used. In addition, future projects may also examine the diagnostic and prognostic value of multimodal neuroimaging data, possibly combined with other clinical, neurocognitive, genetic information.
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Long, Xiaojing. "Image Classification using Pair-wise Registration and Machine Learning with Applications to Neuroimaging." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/40396.

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Alzheimer's disease~(AD) is the most frequent neurodegenerative dementia and a growing health problem. Early and accurate diagnosis and prediction of AD is crucial because treatment may be most efficacious if introduced as early as possible. Neuropsychological testing, which is clinically used, sometimes fails to recognize probable dementia, especially to recognize the disease at an early time point such as the mild cognitive impairment~(MCI), which is the prodromal stage of AD. Recently, there has been a realization that magnetic resonance imaging~(MRI) may help diagnoses of AD and MCI. In this dissertation, we introduce an MRI-analysis based algorithm to help diagnose the illness before irreversible neuronal loss has set in, and to help detect brain changes between MCI patients who may convert and may not convert to AD. Given a set of brain MR images, the algorithm first calculates the distance between each pair of images via a registration process. Then images are projected from a high dimensional Euclidean space to a low dimensional Euclidean subspace based on the calculated distances, with a dimension reduction method. Finally classical supervised classification approaches are employed to assign images to appropriate groups in the low dimensional space. The classification accuracy rates we obtained in our experiments are higher than, or at least comparable to, those reported in recently published papers. Moreover, this algorithm can be extended to explore the pathology distribution of AD. Exploring the distribution of AD pathology is of great importance to reveal AD related regional atrophy at specific stages of the disease and provide insight into longitudinal sequence of disease progression. Calculating distances between different brain structures produces different classification accuracy. Those structures yielding higher classification accuracy are considered as pathological regions. Our experimental results on pathology localization are also compared with the reproduced results using other existing popular algorithms; the observations are consistent.
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Lembke, Benjamin. "Bearing Diagnosis Using Fault Signal Enhancing Teqniques and Data-driven Classification." Thesis, Linköpings universitet, Fordonssystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158240.

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Rolling element bearings are a vital part in many rotating machinery, including vehicles. A defective bearing can be a symptom of other problems in the machinery and is due to a high failure rate. Early detection of bearing defects can therefore help to prevent malfunction which ultimately could lead to a total collapse. The thesis is done in collaboration with Scania that wants a better understanding of how external sensors such as accelerometers, can be used for condition monitoring in their gearboxes. Defective bearings creates vibrations with specific frequencies, known as Bearing Characteristic Frequencies, BCF [23]. A key component in the proposed method is based on identification and extraction of these frequencies from vibration signals from accelerometers mounted near the monitored bearing. Three solutions are proposed for automatic bearing fault detection. Two are based on data-driven classification using a set of machine learning methods called Support Vector Machines and one method using only the computed characteristic frequencies from the considered bearing faults. Two types of features are developed as inputs to the data-driven classifiers. One is based on the extracted amplitudes of the BCF and the other on statistical properties from Intrinsic Mode Functions generated by an improved Empirical Mode Decomposition algorithm. In order to enhance the diagnostic information in the vibration signals two pre-processing steps are proposed. Separation of the bearing signal from masking noise are done with the Cepstral Editing Procedure, which removes discrete frequencies from the raw vibration signal. Enhancement of the bearing signal is achieved by band pass filtering and amplitude demodulation. The frequency band is produced by the band selection algorithms Kurtogram and Autogram. The proposed methods are evaluated on two large public data sets considering bearing fault classification using accelerometer data, and a smaller data set collected from a Scania gearbox. The produced features achieved significant separation on the public and collected data. Manual detection of the induced defect on the outer race on the bearing from the gearbox was achieved. Due to the small amount of training data the automatic solutions were only tested on the public data sets. Isolation performance of correct bearing and fault mode among multiplebearings were investigated. One of the best trade offs achieved was 76.39 % fault detection rate with 8.33 % false alarm rate. Another was 54.86 % fault detection rate with 0 % false alarm rate.
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Wen, Junhao. "Structural and microstructural neuroimaging for diagnosis and tracking of neurodegenerative diseases." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS415.

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L’identification et le suivi de biomarqueur de la démence sont essentiels pour mieux comprendre les mécanismes pathologiques et la trajectoire de la maladie. Le diagnostic précoce de la démence constitue un autre défi. Cette thèse a deux objectifs principaux. Premièrement, nous cherchons à identifier les biomarqueurs les plus prometteurs au stade présymptomatique de la démence. Plus spécifiquement, nous avons étudié ce phénomène dans le cas de la dégénérescence lobaire frontotemporale due à la mutation C9orf72. Le deuxième objectif est de faire progresser le diagnostic et le pronostic précoces en utilisant des méthodes d’apprentissage machine et des données d’imagerie par résonance magnétique. Nous abordons cette question dans le contexte de la maladie d’Alzheimer sporadique. Suivant ces deux objectifs, la thèse se compose de deux parties principales, chaque partie comprenant deux études. Dans la première étude, les biomarqueurs ont été identifiés à partir de l’IRM conventionnelle pondérée T1 et du modèle d’imagerie du tenseur de diffusion. La deuxième étude a comparé la sensibilité et la spécificité du modèle NODDI et celle de techniques conventionnelles, à savoir l’IRM pondérée en T1 et le DTI. La deuxième partie porte sur le diagnostic précoce de la MA et comprend les deux dernières études. La troisième étude propose un cadre open source pour une évaluation reproductible de la classification de la MA à l’aide de l’IRM de diffusion et des méthodes classiques d’apprentissage. La dernière étude étend ce cadre aux méthodes d’apprentissage profond et démontre son utilisation sur l’IRM pondérée en T1
Biomarker identification and tracking in dementia are essential to better understand the pathological mechanism and disease trajectory. The current PhD aims has two main objectives. First, we aim to identify the most promising biomarkers at the presymptomatic stage of dementia. More specifically, we studied this in the case of genetic frontotemporal lobar degeneration due to C9orf72 mutation. The second objective is to advance early diagnosis and prognosis by using machine learning methods with magnetic resonance imaging data. We tackle this in the context of sporadic Alzheimer’s disease. According to these two objectives, the thesis consists of two main parts, each part comprising two studies. In the first study, biomarkers were identified from conventional T1-weighted MRI and diffusion tensor imaging model. The second study compared the sensitivity and specificity of the advanced NODDI model and to that of conventional techniques, namely T1-weighted MRI and DTI. The second part focuses on early diagnosis of AD and comprises the last two studies. The third study proposes an open source framework for reproducible evaluation of AD classification using diffusion MRI and conventional ML methods. The last study extends this framework to deep learning methods and demonstrates its use on T1-weighted MRI
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El, Azami Meriem. "Computer aided diagnosis of epilepsy lesions based on multivariate and multimodality data analysis." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI087/document.

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Environ 150.000 personnes souffrent en France d'une épilepsie partielle réfractaire à tous les médicaments. La chirurgie, qui constitue aujourd’hui le meilleur recours thérapeutique nécessite un bilan préopératoire complexe. L'analyse de données d'imagerie telles que l’imagerie par résonance magnétique (IRM) anatomique et la tomographie d’émission de positons (TEP) au FDG (fluorodéoxyglucose) tend à prendre une place croissante dans ce protocole, et pourrait à terme limiter de recourir à l’électroencéphalographie intracérébrale (SEEG), procédure très invasive mais qui constitue encore la technique de référence. Pour assister les cliniciens dans leur tâche diagnostique, nous avons développé un système d'aide au diagnostic (CAD) reposant sur l'analyse multivariée de données d'imagerie. Compte tenu de la difficulté relative à la constitution de bases de données annotées et équilibrées entre classes, notre première contribution a été de placer l'étude dans le cadre méthodologique de la détection du changement. L'algorithme du séparateur à vaste marge adapté à ce cadre là (OC-SVM) a été utilisé pour apprendre, à partir de cartes multi-paramétriques extraites d'IRM T1 de sujets normaux, un modèle prédictif caractérisant la normalité à l'échelle du voxel. Le modèle permet ensuite de faire ressortir, dans les images de patients, les zones cérébrales suspectes s'écartant de cette normalité. Les performances du système ont été évaluées sur des lésions simulées ainsi que sur une base de données de patients. Trois extensions ont ensuite été proposées. D'abord un nouveau schéma de détection plus robuste à la présence de bruit d'étiquetage dans la base de données d'apprentissage. Ensuite, une stratégie de fusion optimale permettant la combinaison de plusieurs classifieurs OC-SVM associés chacun à une séquence IRM. Enfin, une généralisation de l'algorithme de détection d'anomalies permettant la conversion de la sortie du CAD en probabilité, offrant ainsi une meilleure interprétation de la sortie du système et son intégration dans le bilan pré-opératoire global
One third of patients suffering from epilepsy are resistant to medication. For these patients, surgical removal of the epileptogenic zone offers the possibility of a cure. Surgery success relies heavily on the accurate localization of the epileptogenic zone. The analysis of neuroimaging data such as magnetic resonance imaging (MRI) and positron emission tomography (PET) is increasingly used in the pre-surgical work-up of patients and may offer an alternative to the invasive reference of Stereo-electro-encephalo -graphy (SEEG) monitoring. To assist clinicians in screening these lesions, we developed a computer aided diagnosis system (CAD) based on a multivariate data analysis approach. Our first contribution was to formulate the problem of epileptogenic lesion detection as an outlier detection problem. The main motivation for this formulation was to avoid the dependence on labelled data and the class imbalance inherent to this detection task. The proposed system builds upon the one class support vector machines (OC-SVM) classifier. OC-SVM was trained using features extracted from MRI scans of healthy control subjects, allowing a voxelwise assessment of the deviation of a test subject pattern from the learned patterns. System performance was evaluated using realistic simulations of challenging detection tasks as well as clinical data of patients with intractable epilepsy. The outlier detection framework was further extended to take into account the specificities of neuroimaging data and the detection task at hand. We first proposed a reformulation of the support vector data description (SVDD) method to deal with the presence of uncertain observations in the training data. Second, to handle the multi-parametric nature of neuroimaging data, we proposed an optimal fusion approach for combining multiple base one-class classifiers. Finally, to help with score interpretation, threshold selection and score combination, we proposed to transform the score outputs of the outlier detection algorithm into well calibrated probabilities
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Kindblom, Marie. "Diagnostic prediction on anamnesis in digital primary health care." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231827.

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Primary health care is facing extensive changes due to digitalization, while the field of application for machine learning is expanding. The merging of these two fields could result in a range of outcomes, one of them being an improved and more rigorous adoption of clinical decision support systems. Clinical decision support systems have been around for a long time but are still not fully adopted in primary health care due to insufficient performance and interpretation. Clinical decision support systems have a range of supportive functions to assist the clinician during decision making, where one of the most researched topics is diagnostic support. This thesis investigates how the use of self-described anamnesis in the form of free text and multiple-choice questions performs in prediction of diagnostic outcome. The chosen approach is to compare text to different subsets of multiple-choice questions for diagnostic prediction on a range of classification methods. The results indicate that text data holds a substantial amount of information, and that the multiple-choice questions used in this study are of varying quality, yet suboptimal compared to text data. The over-all tendency is that Support Vector Machines perform well on text classification and that Random Forests and Naive Bayes have equal performance to Support Vector Machines on multiple-choice questions.
Primärvården förväntas genomgå en utbredd digitalisering under de kommande åren, samtidigt som maskininlärning får utökade tillämpningsområden. Sammanslagningen av dessa två fält möjliggör en mängd förbättrade tekniker, varav en vore ett förbättrat och mer rigoröst anammande av kliniska beslutsstödsystem. Det har länge funnits varianter av kliniska beslutsstödsystem, men de har ännu inte lyckats blivit fullständigt inkorporerade i primärvården, framför allt på̊ grund av bristfällig prestanda och förmåga till tolkning. Kliniskt beslutstöd erbjuder en mängd funktioner för läkare vid beslutsfattning, där ett av de mest uppmärksammade fälten inom forskningen är support vid diagnosticering. Denna uppsats ämnar att undersöka hur självbeskriven anamnes i form av fritext och flervalsfrågor presterar för förutsägning av diagnos. Det valda tillvägagångssättet har varit att jämföra text med olika delmängder av flervalsfrågor med hjälp av en mängd metoder för klassificering. Resultaten indikerar att textdatan innehåller en avsevärt större mängd information än flervalsfrågorna, samt att flervalsfrågorna som har använts i denna studie är av varierande kvalité, men generellt sett suboptimala vad gäller prestanda i jämförelse med textdatan. Den generella tendensen är att Support Vector Machines presterar bra för klassificering med text data medan Random Forests och Naive Bayes är likvärdiga alternativ till Support Vector Machines för predicering vid användning av flervalsfrågor.
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Book chapters on the topic "Machine Learning, SVM, MRI, diagnosis"

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Samanta, Atanu K., and Asim Ali Khan. "Computer Aided Diagnostic System for Automatic Detection of Brain Tumor Through MRI Using Clustering Based Segmentation Technique and SVM Classifier." In The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), 343–51. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74690-6_34.

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Shams, Seyedmohammad, Esmaeil Davoodi-Bojd, and Hamid Soltanian-Zadeh. "Analysis of Structural MRI Data for Epilepsy Diagnosis Using Machine Learning Techniques." In Machine Learning in Medicine, 77–107. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781315101323-6.

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Choi, Jun-Sik, Eunho Lee, and Heung-Il Suk. "Regional Abnormality Representation Learning in Structural MRI for AD/MCI Diagnosis." In Machine Learning in Medical Imaging, 64–72. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00919-9_8.

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Silva, Aristófanes Corrêa, Anselmo Cardoso de Paiva, and Alexandre Cesar Muniz de Oliveira. "Comparison of FLDA, MLP and SVM in Diagnosis of Lung Nodule." In Machine Learning and Data Mining in Pattern Recognition, 285–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11510888_28.

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Yadav, Samir Shrihari, and Sanjay Raghunath Sutar. "Alzheimer’s Disease Diagnosis Using Structural MRI and Machine Learning Techniques." In Lecture Notes in Electrical Engineering, 645–65. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5078-9_53.

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Rieke, Johannes, Fabian Eitel, Martin Weygandt, John-Dylan Haynes, and Kerstin Ritter. "Visualizing Convolutional Networks for MRI-Based Diagnosis of Alzheimer’s Disease." In Understanding and Interpreting Machine Learning in Medical Image Computing Applications, 24–31. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02628-8_3.

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Lee, Jaa-Yeon, Min A. Yoon, Choong Guen Chee, Jae Hwan Cho, Jin Hoon Park, and Sung-Hong Park. "Metal Artifact Correction MRI Using Multi-contrast Deep Neural Networks for Diagnosis of Degenerative Spinal Diseases." In Machine Learning for Medical Image Reconstruction, 44–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17247-2_5.

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Naren, J., Praveena Ramalingam, U. Raja Rajeswari, P. Vijayalakshmi, and G. Vithya. "An Intelligent System on Computer-Aided Diagnosis for Parkinson’s Disease with MRI Using Machine Learning." In Learning and Analytics in Intelligent Systems, 159–65. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39033-4_16.

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Herzog, Nitsa J., and George D. Magoulas. "Machine Learning-Supported MRI Analysis of Brain Asymmetry for Early Diagnosis of Dementia." In Studies in Computational Intelligence, 29–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91103-4_3.

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Ong, Hong, Hoang Le, Hoang Nguyen, Dong Nguyen, Huong Ha, Hoan Thanh Ngo, and Nguyen Thanh Duc. "A Machine Learning Framework Based on Extreme Gradient Boosting for Intelligent Alzheimer’s Disease Diagnosis Using Structure MRI." In IFMBE Proceedings, 815–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75506-5_66.

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Conference papers on the topic "Machine Learning, SVM, MRI, diagnosis"

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Yang, Xiaofeng, Jason J. Jeong, Yang Lei, Tian Liu, Walter J. Curran, Hui Mao, Ge Cui, and Tonghe Wang. "Machine-learning-based classification of Glioblastoma using MRI-based radiomic features." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Kensaku Mori. SPIE, 2019. http://dx.doi.org/10.1117/12.2513110.

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Carras, Peter, Carina Pereira, Debosmita Biswas, Christoph Lee, Savannah Partridge, and Adam Alessio. "Genetic algorithm for machine learning architecture selection for breast MRI classification." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Maciej A. Mazurowski. SPIE, 2020. http://dx.doi.org/10.1117/12.2547490.

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Vieceli, Michael, Amy Van Dusen, Karen Drukker, Hiroyuki Abe, Maryellen L. Giger, and Heather M. Whitney. "Case-based repeatability of machine learning classification performance on breast MRI." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Maciej A. Mazurowski. SPIE, 2020. http://dx.doi.org/10.1117/12.2548144.

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Li, Wei, Wei Kuang, Yun Li, Yu-Jing Li, and Wei-Ping Ye. "Clinical X-Ray Image Based Tooth Decay Diagnosis using SVM." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370404.

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Sahebzamani, Ghazal, Mansour Saffar, and Hamid Soltanian-Zadeh. "Machine Learning Based Analysis of Structural MRI for Epilepsy Diagnosis." In 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA). IEEE, 2019. http://dx.doi.org/10.1109/pria.2019.8785985.

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Wang, Chao-yong, Chun-guo Wu, Yan-chun Liang, and Xin-chen Guo. "Diagnosis of Breast Cancer Tumor Based on ICA and LS-SVM." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258850.

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Sun, Hui-Qin, Zhi-Hong Xue, Yun Du, Li-Hua Sun, and Ke-Jun Sun. "Power transformer fault diagnosis based on fuzzy C-means clustering and multi-class SVM." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580647.

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Zou, Chao, En-hui Zheng, Hong-wei Xu, and Le Chen. "SVM-Based Multiclass Cost-sensitive Classification with Reject Option for Fault Diagnosis of Steam Turbine Generator." In 2010 Second International Conference on Machine Learning and Computing. IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.26.

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An, Jin-Long, and Zhen-Ping Ma. "Study on the method of fault diagnosis in analog circuits based on new multi-class SVM." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580826.

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Jin-Zhuang Xiao, Hong-Rui Wang, and Zheng Gao. "Notice of Retraction: Diagnosis method for connection-related faults in motion system based on SVM." In 2009 Eighth International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212383.

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