Journal articles on the topic 'Chest X-ray image'
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Altun, Armagan, and Okan Erdogan. "A Chest X-Ray Image." Cardiology in Review 11, no. 6 (November 2003): 301–2. http://dx.doi.org/10.1097/01.crd.0000089526.05199.04.
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Wu, Huaiguang, Pengjie Xie, Huiyi Zhang, Daiyi Li, and Ming Cheng. "Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 2893–907. http://dx.doi.org/10.3233/jifs-191438.
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The chest X-ray examination is one of the most important methods for screening and diagnosing of many lung diseases. Diagnosis of pneumonia by chest X-ray is one of the common methods used by medical experts. However, the image quality of chest X-Ray has some defects, such as low contrast, overlapping organs and blurred boundary, which seriously affects detecting pneumonia in chest X-rays. Therefore, it has important medical value and application significance to construct a stable and accurate automatic detection model of pneumonia through a large number of chest X-ray images. In this paper, we propose a novel hybrid system for detecting pneumonia from chest X-Ray image: ACNN-RF, which is an adaptive median filter Convolutional Neural Network (CNN) recognition model based on Random forest (RF). Firstly, the improved adaptive median filtering is employed to remove noise in the chest X-ray image, which makes the image more easily recognized. Secondly, we establish the CNN architecture based on Dropout to extract deep activation features from each chest X-ray image. Finally, we employ the RF classifier based on GridSearchCV class as a classifier for deep activation features in CNN model. It not only avoids the phenomenon of over-fitting in data training, but also improves the accuracy of image classification. During our experiment, the public chest X-ray image dataset used in the experiment contains 5863 images, which comprises 4265 frontal-view X-ray images of 1574 unique patients. The average recognition rate of pneumonia is up to 97% by the proposed ACNN-RF. The experimental results show that the ACNN-RF identification system is more effective than the previous traditional image identification system.
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Park, So Yeon, and Byung Cheol Song. "Image Quality Enhancement for Chest X-ray images." Journal of the Institute of Electronics and Information Engineers 52, no. 10 (October 25, 2015): 97–107. http://dx.doi.org/10.5573/ieie.2015.52.10.097.
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Ismail, Azlan, Taufik Rahmat, and Sharifah Aliman. "CHEST X-RAY IMAGE CLASSIFICATION USING FASTER R-CNN." MALAYSIAN JOURNAL OF COMPUTING 4, no. 1 (July 1, 2019): 225. http://dx.doi.org/10.24191/mjoc.v4i1.6095.
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Chest x-ray image analysis is the common medical imaging exam needed to assess different pathologies. Having an automated solution for the analysis can contribute to minimizing the workloads, improve efficiency and reduce the potential of reading errors. Many methods have been proposed to address chest x-ray image classification and detection. However, the application of regional-based convolutional neural networks (CNN) is currently limited. Thus, we propose an approach to classify chest x-ray images into either one of two categories, pathological or normal based on Faster Regional-CNN model. This model utilizes Region Proposal Network (RPN) to generate region proposals and perform image classification. By applying this model, we can potentially achieve two key goals, high confidence in the classification and reducing the computation time. The results show the applied model achieved higher accuracy as compared to the medical representatives on the random chest x-ray images. The classification model is also reasonably effective in classifying between finding and normal chest x-ray image captured through a live webcam.
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Widodo, Chomsin S., Agus Naba, Muhammad M. Mahasin, Yuyun Yueniwati, Terawan A. Putranto, and Pangeran I. Patra. "UBNet: Deep learning-based approach for automatic X-ray image detection of pneumonia and COVID-19 patients." Journal of X-Ray Science and Technology 30, no. 1 (January 22, 2022): 57–71. http://dx.doi.org/10.3233/xst-211005.
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BACKGROUND: Analysis of chest X-ray images is one of the primary standards in diagnosing patients with COVID-19 and pneumonia, which is faster than using PCR Swab method. However, accuracy of using X-ray images needs to be improved. OBJECTIVE: To develop a new deep learning system of chest X-ray images and evaluate whether it can quickly and accurately detect pneumonia and COVID-19 patients. METHODS: The developed deep learning system (UBNet v3) uses three architectural hierarchies, namely first, to build an architecture containing 7 convolution layers and 3 ANN layers (UBNet v1) to classify between normal images and pneumonia images. Second, using 4 layers of convolution and 3 layers of ANN (UBNet v2) to classify between bacterial and viral pneumonia images. Third, using UBNet v1 to classify between pneumonia virus images and COVID-19 virus infected images. An open-source database with 9,250 chest X-ray images including 3,592 COVID-19 images were used in this study to train and test the developed deep learning models. RESULTS: CNN architecture with a hierarchical scheme developed in UBNet v3 using a simple architecture yielded following performance indices to detect chest X-ray images of COVID-19 patients namely, 99.6%accuracy, 99.7%precision, 99.7%sensitivity, 99.1%specificity, and F1 score of 99.74%. A desktop GUI-based monitoring and classification system supported by a simple CNN architecture can process each chest X-ray image to detect and classify COVID-19 image with an average time of 1.21 seconds. CONCLUSION: Using three hierarchical architectures in UBNet v3 improves system performance in classifying chest X-ray images of pneumonia and COVID-19 patients. A simple architecture also speeds up image processing time.
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Kalidasan, S. "COVID-19 Detection with X-Ray and CT-Scan Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 1526–29. http://dx.doi.org/10.22214/ijraset.2022.40096.
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Abstract: We researched the diagnostic capabilities of deep learning on chest radiographs and an image classifier based on the COVID-Net was presented to classify chest MRIimages. In the case of a small amount of COVID-19 data, data enhancement was proposed to expanded COVID-19 data 17 times. Our model aims at transfer learning, model integration and classify chest MRI images according to three labels: normal, COVID-19 and viral pneumonia. According to the accuracy and loss value, choose the models ResNet-101 and ResNet-152 with good effect for fusion, and dynamically improve their weight ratio during the training process. After training, the model can achieve 96.1% of the types of chest MRI images accuracy on the test set. This technology has higher sensitivity than radiologists in the screening and diagnosis of lung nodules. As an auxiliary diagnostic technology, it can help radiologists improve work efficiency and diagnostic accuracy. COVID-19 is posed as very infectious and deadly pneumonia type disease until recent time. Despite having lengthy testing time, RT-PCR is a proven testing methodology to detect corona virus infection. Sometimes, it might give more false positive and false negative results than the desired rates. Therefore, to assist the traditional RT-PCR methodology for accurate clinical diagnosis, COVID-19 screening can be adopted with X-Ray and CT scan images of lung of an individual. This image based diagnosis will bring radical change in detecting corona virus infection in human body with ease and having zero or near to zero false positives and false negatives rates. This paper reports a convolution neural network (CNN) based multi-image augmentation technique for detecting COVID-19 in chest X-Ray and chest CT scan images of corona virus suspected individuals. Multi-image augmentation makes use of discontinuity information obtained in the filtered images for increasing the number of effective examples for training the CNN model. With this approach, the proposed model exhibits higher classification accuracy around 95.38% and 98.97% for CT scan and X-Ray images respectively. CT scan images with multi-image augmentation chieves sensitivity of 94.78% and specificity of 95.98%, whereas X-Ray images with multi-image augmentation achieves sensitivity of 99.07% and specificity of 98.88%. Evaluation has been done on publicly available databases containing both chest X-Ray and CT scan images and the experimental results are also compared with ResNet-50 and VGG-16 models. Keywords: Machine Learning, convolution neural network, MRI images.
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Rumyantsev, Aleksei Aleksandrovich, Farkhad Mansurovich Bikmuratov, and Nikolai Pavlovich Pashin. "Entropy estimation of the fragments of chest X-ray images." Кибернетика и программирование, no. 1 (January 2021): 20–26. http://dx.doi.org/10.25136/2644-5522.2021.1.31676.
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The subject of this research is medical chest X-ray images. After fundamental pre-processing, the accumulated database of such images can be used for training deep convolutional neural networks that have become one of the most significant innovations in recent years. The trained network carries out preliminary binary classification of the incoming images and serve as an assistant to the radiotherapist. For this purpose, it is necessary to train the neural network to carefully minimize type I and type II errors. Possible approach towards improving the effectiveness of application of neural networks, by the criteria of reducing computational complexity and quality of image classification, is the auxiliary approaches: image pre-processing and preliminary calculation of entropy of the fragments. The article provides the algorithm for X-ray image pre-processing, its fragmentation, and calculation of the entropy of separate fragments. In the course of pre-processing, the region of lungs and spine is selected, which comprises approximately 30-40% of the entire image. Then the image is divided into the matrix of fragments, calculating the entropy of separate fragments in accordance with Shannon’s formula based pm the analysis of individual pixels. Determination of the rate of occurrence of each of the 255 colors allows calculating the total entropy. The use of entropy for detecting pathologies is based on the assumption that its values differ for separate fragments and overall picture of its distribution between the images with the norm and pathologies. The article analyzes the statistical values: standard deviation of error, dispersion. A fully connected neural network is used for determining the patterns in distribution of entropy and its statistical characteristics on various fragments of the chest X-ray image.
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Mogaveera, Rachita, Roshan Maur, Zeba Qureshi, and Yogita Mane. "Multi-class Chest X-ray classification of Pneumonia, Tuberculosis and Normal X-ray images using ConvNets." ITM Web of Conferences 44 (2022): 03007. http://dx.doi.org/10.1051/itmconf/20224403007.
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Pneumonia and Tuberculosis (TB) are two serious and life-threatening diseases that are caused by a bacterial or viral infection of the lungs and have the potential to result in severe consequences within a short period of time. Therefore, early diagnosis is a significant factor in terms of a successful treatment process. Chest X-Rays which are used to diagnose Pneumonia and/or Tuberculosis need expert radiologists for evaluation. Thus, there is a need for an intelligent and automatic system that has the capability of diagnosing chest X-rays, and to simplify the disease detection process for experts and novices. This study aims to develop a model that will help with the classification of chest X-ray medical images into normal vs Pneumonia or Tuberculosis. Medical organizations take a minimum of one day to classify the diagnosis, while our model could perform the same classification within a few seconds. Also, it will display a prediction probability about the predicted class. The model had an accuracy, precision and recall score over 90% which indicates that the model was able to identify patterns. Users can upload their respective chest X-ray image and the model will classify the uploaded image into normal vs abnormal.
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H, Roopa, and Asha T. "Feature Extraction of Chest X-ray Images and Analysis using PCA and kPCA." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (October 1, 2018): 3392. http://dx.doi.org/10.11591/ijece.v8i5.pp3392-3398.
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<p class="abstract">Tuberculosis (TB) is an infectious disease caused by mycobacterium which can be diagnosed by its various symptoms like fever, cough, etc. Tuberculosis can also be analyzed by understanding the chest x-ray of the patient which is revealed by an expert physician .The chest x-ray image contains many features which cannot be directly used by any computer system for analyzing the disease. Features of chest x-ray images must be understood and extracted, so that it can be processed to a form to be fed to any computer system for disease analysis. This paper presents feature extraction of chest x-ray image which can be used as an input for any data mining algorithm for TB disease analysis. So texture and shape based features are extracted from x-ray image using image processing concepts. The features extracted are analyzed using principal component analysis (PCA) and kernel principal component analysis (kPCA) techniques. Filter and wrapper feature selection method using linear regression model were applied on these techniques. The performance of PCA and kPCA are analyzed and found that the accuracy of PCA using wrapper approach is 96.07% when compared to the accuracy of kPCA which is 62.50%. PCA performs well than kPCA with a good accuracy.</p>
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Caseneuve, Guy, Iren Valova, Nathan LeBlanc, and Melanie Thibodeau. "Chest X-Ray Image Preprocessing for Disease Classification." Procedia Computer Science 192 (2021): 658–65. http://dx.doi.org/10.1016/j.procs.2021.08.068.
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Xu, Shuaijing, Junqi Guo, Guangzhi Zhang, and Rongfang Bie. "Automated Detection of Multiple Lesions on Chest X-ray Images: Classification Using a Neural Network Technique with Association-Specific Contexts." Applied Sciences 10, no. 5 (March 3, 2020): 1742. http://dx.doi.org/10.3390/app10051742.
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Automated detection of lung lesions on Chest X-ray images shows good performance to reduce lung cancer mortality. However, it is difficult to detect multiple lesions of single image well and truly, and additional efforts are needed to improve diagnostic efficiency and quality. In this paper, a multi-label classification model combining attention-based neural networks and association-specific contexts is proposed for the detection of multiple lesions on chest X-ray images. A convolutional neural network and a long short-term memory network are first aligned by an attention mechanism to take advantage of both image and text information for the detection, called CNN-ATTENTION-LSTM (CAL) network. In addition, a mining method of implicit association strength to obtain an association network of chest lesions (CLA) network is designed to guide the training of CAL network. The CLA network provides possible clinical relationships between lesions to help the CAL network obtain better predictions. Experimental results on ChestX-ray14 dataset show that our method outperforms some state-of-the-art models under the metrics of area under curve (AUC), precision, recall, and F-score and achieves up to 85.4% in the case of atelectasis and infiltration. It indicates that the method may be useful in the computer-aided detection of multiple lesions on chest X-ray images.
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Badawi, Abeer, and Khalid Elgazzar. "Detecting Coronavirus from Chest X-rays Using Transfer Learning." COVID 1, no. 1 (September 18, 2021): 403–15. http://dx.doi.org/10.3390/covid1010034.
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Coronavirus disease (COVID-19) is an illness caused by a novel coronavirus family. One of the practical examinations for COVID-19 is chest radiography. COVID-19 infected patients show abnormalities in chest X-ray images. However, examining the chest X-rays requires a specialist with high experience. Hence, using deep learning techniques in detecting abnormalities in the X-ray images is presented commonly as a potential solution to help diagnose the disease. Numerous research has been reported on COVID-19 chest X-ray classification, but most of the previous studies have been conducted on a small set of COVID-19 X-ray images, which created an imbalanced dataset and affected the performance of the deep learning models. In this paper, we propose several image processing techniques to augment COVID-19 X-ray images to generate a large and diverse dataset to boost the performance of deep learning algorithms in detecting the virus from chest X-rays. We also propose innovative and robust deep learning models, based on DenseNet201, VGG16, and VGG19, to detect COVID-19 from a large set of chest X-ray images. A performance evaluation shows that the proposed models outperform all existing techniques to date. Our models achieved 99.62% on the binary classification and 95.48% on the multi-class classification. Based on these findings, we provide a pathway for researchers to develop enhanced models with a balanced dataset that includes the highest available COVID-19 chest X-ray images. This work is of high interest to healthcare providers, as it helps to better diagnose COVID-19 from chest X-rays in less time with higher accuracy.
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Meshram, Shruti. "Pneumonia Detection using Chest X-Ray Images." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1611–18. http://dx.doi.org/10.22214/ijraset.2021.35294.
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Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pre-trained on Image Net, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pre-trained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children’s Medical Center dataset.
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Irmak, Emrah. "Implementation of convolutional neural network approach for COVID-19 disease detection." Physiological Genomics 52, no. 12 (December 1, 2020): 590–601. http://dx.doi.org/10.1152/physiolgenomics.00084.2020.
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In this paper, two novel, powerful, and robust convolutional neural network (CNN) architectures are designed and proposed for two different classification tasks using publicly available data sets. The first architecture is able to decide whether a given chest X-ray image of a patient contains COVID-19 or not with 98.92% average accuracy. The second CNN architecture is able to divide a given chest X-ray image of a patient into three classes (COVID-19 versus normal versus pneumonia) with 98.27% average accuracy. The hyperparameters of both CNN models are automatically determined using Grid Search. Experimental results on large clinical data sets show the effectiveness of the proposed architectures and demonstrate that the proposed algorithms can overcome the disadvantages mentioned above. Moreover, the proposed CNN models are fully automatic in terms of not requiring the extraction of diseased tissue, which is a great improvement of available automatic methods in the literature. To the best of the author’s knowledge, this study is the first study to detect COVID-19 disease from given chest X-ray images, using CNN, whose hyperparameters are automatically determined by the Grid Search. Another important contribution of this study is that it is the first CNN-based COVID-19 chest X-ray image classification study that uses the largest possible clinical data set. A total of 1,524 COVID-19, 1,527 pneumonia, and 1524 normal X-ray images are collected. It is aimed to collect the largest number of COVID-19 X-ray images that exist in the literature until the writing of this research paper.
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Singhal, Prateek, Pawan Singh, and Ankit Vidyarthi. "Interpretation and localization of Thorax diseases using DCNN in Chest X-Ray." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 1, no. 1 (April 24, 2020): 1–7. http://dx.doi.org/10.54060/jieee/001.01.001.
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In recent years, the use of diagnosing images has been increased dramatically. An entry-level task of diagnosing and reading Chest X-ray for radiologist but they ought to require a good knowledge and careful observation of anatomical principles, pathology and physiology for this complex reasonings. In many modern hospital’s, the tremendous number of x-ray images are stored in PACS (Picture Archiving and Communication System). The conditions of plethora been diagnosed by the sustainable number of chest X-Ray. Our aim is to predict the thorax disease categories through deep learning using chest x-rays and their first-pass specialist accuracy. In a paper, the main application that presents a pathology localization framework and multi-label unified weakly supervised image classification that can perceive the occurrence of afterward generation of the bounding box around the consistent and multiple pathologies. Due to considering of large image capacity, we adapt Deep Convolutional Neural Network (DCNN) architecture for weakly-supervised object localization, different pooling strategies, various multi-label CNN losses and measured against a baseline of softmax regression.
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Xu, Jing, Hui Li, and Xiu Li. "MS-ANet: deep learning for automated multi-label thoracic disease detection and classification." PeerJ Computer Science 7 (May 17, 2021): e541. http://dx.doi.org/10.7717/peerj-cs.541.
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The chest X-ray is one of the most common radiological examination types for the diagnosis of chest diseases. Nowadays, the automatic classification technology of radiological images has been widely used in clinical diagnosis and treatment plans. However, each disease has its own different response characteristic receptive field region, which is the main challenge for chest disease classification tasks. Besides, the imbalance of sample data categories further increases the difficulty of tasks. To solve these problems, we propose a new multi-label chest disease image classification scheme based on a multi-scale attention network. In this scheme, multi-scale information is iteratively fused to focus on regions with a high probability of disease, to effectively mine more meaningful information from data. A novel loss function is also designed to improve the rationality of visual perception and multi-label image classification, which forces the consistency of attention regions before and after image transformation. A comprehensive experiment was carried out on the Chest X-Ray14 and CheXpert datasets, separately containing over 100,000 frontal-view and 200,000 front and side view X-ray images with 14 diseases. The AUROC is 0.850 and 0.815 respectively on the two data sets, which achieve the state-of-the-art results, verified the effectiveness of this method in chest X-ray image classification. This study has important practical significance for using AI algorithms to assist radiologists in improving work efficiency and diagnostic accuracy.
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Adi, Prajanto Wahyu, Fajar Agung Nugroho, and Yani Parti Astuti. "New Image Texture Feature for Chest X-Ray Classification." Journal of Applied Intelligent System 7, no. 1 (May 19, 2022): 8–15. http://dx.doi.org/10.33633/jais.v7i1.5340.
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This study proposes a new feature extraction model to identify CXR images of covid-19 and pneumonia has a high visual resemblance. The feature extraction model starts by using histogram equalization and average filters as lowpass features and high pass features obtained through Laplacian and LoG filters. In the next step, covariance matrix of image along with the entire features are used to produce an eigen vector that will be used as a feature vector in the classification process. The final stage is the process of testing features on the classification algorithms KNN, SVM, LDA, Naïve Bayes, and Decision Tree through a 10-foldcross validation scheme with 0.9 training data and 0.1 test data. The first experiment for the Covid-19 and normal classes shows that the proposed model is able to produce an accuracy of 96% as the comparison model with GLCM texture extraction have an accuracy value of 91%. The second test is conducted for the class Covid-19 and pneumonia and obtained an accuracy value of 89% for the proposed model and 73% for the GLCM texture extraction. Experiments proved that the proposed model successfully outperformed the GLCM texture extraction model in all of classification algorithms used.
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Filist, S. A., R. A. Tomakova, S. V. Degtyarev, and A. F. Rybochkin. "Hybrid Intelligent Models for Chest X-Ray Image Segmentation." Biomedical Engineering 51, no. 5 (January 2018): 358–63. http://dx.doi.org/10.1007/s10527-018-9748-5.
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Giełczyk, Agata, Anna Marciniak, Martyna Tarczewska, and Zbigniew Lutowski. "Pre-processing methods in chest X-ray image classification." PLOS ONE 17, no. 4 (April 5, 2022): e0265949. http://dx.doi.org/10.1371/journal.pone.0265949.
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Background The SARS-CoV-2 pandemic began in early 2020, paralyzing human life all over the world and threatening our security. Thus, the need for an effective, novel approach to diagnosing, preventing, and treating COVID-19 infections became paramount. Methods This article proposes a machine learning-based method for the classification of chest X-ray images. We also examined some of the pre-processing methods such as thresholding, blurring, and histogram equalization. Results We found the F1-score results rose to 97%, 96%, and 99% for the three analyzed classes: healthy, COVID-19, and pneumonia, respectively. Conclusion Our research provides proof that machine learning can be used to support medics in chest X-ray classification and improving pre-processing leads to improvements in accuracy, precision, recall, and F1-scores.
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Wikanargo, Matheus Alvian, and Angelina Pramana Thenata. "IMAGE SEGMENTATION OF CHEST X-RAYS FOR ABNORMALITY PATTERN RECOGNATION IN LUNGS USING FUZZY C-MEANS METHOD." Jurnal Terapan Teknologi Informasi 2, no. 2 (October 19, 2018): 13–23. http://dx.doi.org/10.21460/jutei.2018.22.98.
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The lungs are one of the important and vital organs in the body that function as a respiratory system process. One way to detect lung disease is to do an X-rays test. Chest X-ray is a radiographic projection to detect abnormalities in lung organ by using x-ray radiation. In the process of diagnosing, doctors see the condition of the results of Chest X-rays in the form of a thorax image (chest) to know the patient has an abnormal or normal lung. However, doctors' diagnosis of chest X-rays results-based abnormalities is likely to differ depending on the doctor's abilities and experience. This problem is expected to be solved by segmenting the lung image to help make the diagnosis appropriately. The purpose of this study is to conduct an analysis that can differentiate abnormal and normal lungs. The process of recognition of these patterns consists of the pre-processing stage of image segmentation by using morphology and then proceed to grouping by using fuzzy c-means method to express the pattern of the already segmented image. This research produces normal and abnormal lung images that can be identified with an accuracy of 80%.
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Azizah, Fani Nur, and Dwi Juniati. "ANALISIS JENIS PENYAKIT PARU-PARU BERDASARKAN CHEST X-RAY MENGGUNAKAN METODE FUZZY C-MEANS." MATHunesa: Jurnal Ilmiah Matematika 9, no. 2 (August 31, 2021): 322–31. http://dx.doi.org/10.26740/mathunesa.v9n2.p322-331.
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Lungs are vital organs that easily infected making them susceptible to diseases, such as atelectasis,effusion, pneumothorax and cancer. Diseases in the lungs can be detected using x-ray. Based onmedicaltheory, the results of the x-ray images of the lung diseases are difficult for ordinary people toread.. This research analyzes the x-ray image of the lungs to make easier the process of analysis. Theanalysis will be easy to carry out if the charaxteristic is known. In this case, fractal dimensions wereimplemented to clustering the typse if lung disesase based on chest x-ray. There are 100 x-ray image ofthe lungs that will be processed using segmentation. Result of segmentation is a region of the lungs.These regions are used in Canny edge detection to find out spots of lung disease. Then the dimensionvalue is calculated using box counting so that it can be clustered. The results of the experiment using thefuzzy c-means method with four clusters have an accuracy of 86%.
Keywords : Chest X-ray, Box Counting, Fuzzy C-Means
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Maksum, Vivin Umrotul M., Dian C. Rini Novitasari, and Abdulloh Hamid. "Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM." Jurnal Matematika MANTIK 7, no. 1 (May 31, 2021): 74–85. http://dx.doi.org/10.15642/mantik.2021.7.1.74-85.
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COVID-19 is a disease or virus that has recently spread worldwide. The disease has also taken many casualties because the virus is notoriously deadly. An examination can be carried out using a chest X-Ray because it costs cheaper compared to swab and PCR tests. The data used in this study was chest X-Ray image data. Chest X-Ray images can be identified using Computer-Aided Diagnosis by utilizing machine learning classification. The first step was the preprocessing stage and feature extraction using the Gray Level Co-Occurrence Matrix (GLCM). The result of the feature extraction was then used at the classification stage. The classification process used was Extreme Learning Machine (ELM). Extreme Learning Machine (ELM) is one of the artificial neural networks with advanced feedforward which has one hidden layer called Single Hidden Layer Feedforward Neural Networks (SLFNs). The results obtained by GLCM feature extraction and classification using ELM achieved the best accuracy of 91.21%, the sensitivity of 100%, and the specificity of 91% at 135° rotation using linear activation function with 15 hidden nodes.
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Gopatoti, Anandbabu, and P. Vijayalakshmi. "Optimized chest X-ray image semantic segmentation networks for COVID-19 early detection." Journal of X-Ray Science and Technology 30, no. 3 (April 15, 2022): 491–512. http://dx.doi.org/10.3233/xst-211113.
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BACKGROUND: Although detection of COVID-19 from chest X-ray radiography (CXR) images is faster than PCR sputum testing, the accuracy of detecting COVID-19 from CXR images is lacking in the existing deep learning models. OBJECTIVE: This study aims to classify COVID-19 and normal patients from CXR images using semantic segmentation networks for detecting and labeling COVID-19 infected lung lobes in CXR images. METHODS: For semantically segmenting infected lung lobes in CXR images for COVID-19 early detection, three structurally different deep learning (DL) networks such as SegNet, U-Net and hybrid CNN with SegNet plus U-Net, are proposed and investigated. Further, the optimized CXR image semantic segmentation networks such as GWO SegNet, GWO U-Net, and GWO hybrid CNN are developed with the grey wolf optimization (GWO) algorithm. The proposed DL networks are trained, tested, and validated without and with optimization on the openly available dataset that contains 2,572 COVID-19 CXR images including 2,174 training images and 398 testing images. The DL networks and their GWO optimized networks are also compared with other state-of-the-art models used to detect COVID-19 CXR images. RESULTS: All optimized CXR image semantic segmentation networks for COVID-19 image detection developed in this study achieved detection accuracy higher than 92%. The result shows the superiority of optimized SegNet in segmenting COVID-19 infected lung lobes and classifying with an accuracy of 98.08% compared to optimized U-Net and hybrid CNN. CONCLUSION: The optimized DL networks has potential to be utilised to more objectively and accurately identify COVID-19 disease using semantic segmentation of COVID-19 CXR images of the lungs.
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Jamil, Ahmad Mochtar, Muslim Andala Putra, and Muhammad Anas. "Adobe Photoshop Express Application Enhances the Diagnosis of X-Ray Thorax of Covid-19 Patient." Indonesian Journal of Medical Sciences and Public Health 1, no. 2 (October 19, 2021): 54–61. http://dx.doi.org/10.11594/ijmp.01.02.05.
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Covid-19 is a new infectious viral illness. The first one appeared in Wuhan, and within two months, it became a pandemic. Medical diagnosis is confirmed by fever, cough, shortness of breath, combined with neutrophil ratio lymphocyte analysis and chest x-ray or chest C.T. radiology imaging, with a ground-glass appearance. C.T. scans are not widely available in hospitals in Indonesia. Many hospitals only own x-ray for covid-19 as radiologic diagnostic imaging. With digital imaging capabilities, Due to the similarity of applications such as the radiological workstation, Adobe Photoshop Express will improve the capacity to diagnose Covid-19 from a chest x-ray. Adobe Photoshop Express has outstanding digital processing capabilities to enhance the presentation of images so that the efficiency of diagnosing plain x-ray thorax image cases with Covid-19 becomes easier and more manageable.
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Staroverov, N. E., A. Y. Gryaznov, I. G. Kamyshanskaya, N. N. Potrakhov, and E. D. Kholopova. "The method of increasing the information content of microfocus X-ray images." Russian Technological Journal 9, no. 6 (December 2, 2021): 57–63. http://dx.doi.org/10.32362/2500-316x-2021-9-6-57-63.
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A method for processing microfocus X-ray images is described. It is based on high-frequency filtration and morphological image processing, which increases the contrast of the X-ray details. One of the most informative X-ray techniques is microfocus X-ray. In some cases, microfocus X-ray images cannot be reliably analyzed due to the peculiarities of the shooting method. So, the main disadvantages of microfocus X-ray images are most often an uneven background, distorted brightness characteristics and the presence of noise. The proposed method for enhancing the contrast of fine image details is based on the idea of combining high-frequency filtering and morphological image processing. The method consists of the following steps: noise suppression in the image, high-frequency filtering, morphological image processing, obtaining the resulting image. As a result of applying the method, the brightness of the contours in the image is enhanced. In the resulting image, all objects will have double outlines. The method was tested in the processing of 50 chest radiographs of patients with various pathologies. Radiographs were performed at the Mariinsky Hospital of St. Petersburg using digital stationary and mobile X-ray machines. In most of the radiographs, it was possible to improve the images contrast, to highlight the objects boundaries. Besides, the method was applied in microfocus X-ray tomography to improve the information content of projection data and improve the reconstruction of the 3D image of the research object. In both the first and second cases, the method showed satisfactory results. The developed method makes it possible to significantly increase the information content of microfocus X-ray images. The obtained practical results make it possible to count on broad prospects for the method application, especially in microfocus X-ray.
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Songram, Panida, Chatklaw Jareanpon, Phatthanaphong Chomphuwiset, Khanabhorn Kawattikul, and Chatklaw Jareanpon. "Classification of chest X-ray images using a hybrid deep learning method." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (February 1, 2022): 867. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp867-874.
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This work presents a technique for classifying X-ray images of the chest (CXR) by applying deep learning-based techniques. The CXR will be classified into three different types, i.e. (i) normal, (ii) COVID-19, and (iii) pneumonia. The classification challenge is raised when the X-ray images of COVID-19 and pneumonia are subtle. The CXR images of the chest are first proceeded to be standardized and to improve the visual contrast of the images. Then, the classification is performed by applying a deep learningbased technique that binds two deep learning network architectures, i.e., convolution neural network (CNN) and long short-term memory (LSTM), to generate a hybrid model for the classification problem. The deep features of the images are extracted by CNN before the final classification is performed using LSTM. In addition to the hybrid models, this work explores the validity of image pre-processing methods that improve the quality of the images before the classification is performed. The experiments were conducted on a public image dataset. The experimental results demonstrate that the proposed technique provides promising results and is superior to the baseline techniques.
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Satyo, Adhitio Satyo Bayangkari Karno, Dodi Arif, Indra Sari Kusuma Wardhana, and Eka Sally Moreta. "Diagnosa COVID-19 Chest X-Ray Menggunakan Arsitektur Inception Resnet." Journal of Informatic and Information Security 2, no. 1 (July 3, 2021): 57–66. http://dx.doi.org/10.31599/jiforty.v2i1.646.
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The availability of medical aids in adequate quantities is very much needed to assist the work of the medical staff in dealing with the very large number of Covid patients. Artificial Intelligence (AI) with the Deep Learning (DL) method, especially the Convolution Neural Network (CNN), is able to diagnose Chest X-ray images generated by the Computer Tomography Scanner (C.T. Scan) against certain diseases (Covid). Inception Resnet Version 2 architecture was used in this study to train a dataset of 4000 images, consisting of 4 classifications namely covid, normal, lung opacity and viral pneumonia with 1,000 images each. The results of the study with 50 epoch training obtained very good values for the accuracy of training and validation of 95.5% and 91.8%, respectively. The test with 4000 image dataset obtained 98% accuracy testing, with the precision of each class being Covid (99%), Lung_Opacity (97%), Normal (99%) and Viral pneumonia (99%).
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Sethy, Prabira Kumar, Santi Kumari Behera, Komma Anitha, Chanki Pandey, and M. R. Khan. "Computer aid screening of COVID-19 using X-ray and CT scan images: An inner comparison." Journal of X-Ray Science and Technology 29, no. 2 (March 11, 2021): 197–210. http://dx.doi.org/10.3233/xst-200784.
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The objective of this study is to conduct a critical analysis to investigate and compare a group of computer aid screening methods of COVID-19 using chest X-ray images and computed tomography (CT) images. The computer aid screening method includes deep feature extraction, transfer learning, and machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN models. The machine learning approach includes three sets of handcrafted features and three classifiers. The pre-trained CNN models include AlexNet, GoogleNet, VGG16, VGG19, Densenet201, Resnet18, Resnet50, Resnet101, Inceptionv3, Inceptionresnetv2, Xception, MobileNetv2 and ShuffleNet. The handcrafted features are GLCM, LBP & HOG, and machine learning based classifiers are KNN, SVM & Naive Bayes. In addition, the different paradigms of classifiers are also analyzed. Overall, the comparative analysis is carried out in 65 classification models, i.e., 13 in deep feature extraction, 13 in transfer learning, and 39 in the machine learning approaches. Finally, all classification models perform better when applying to the chest X-ray image set as comparing to the use of CT scan image set. Among 65 classification models, the VGG19 with SVM achieved the highest accuracy of 99.81%when applying to the chest X-ray images. In conclusion, the findings of this analysis study are beneficial for the researchers who are working towards designing computer aid tools for screening COVID-19 infection diseases.
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Yu, Yue, Kun She, and Jinhua Liu. "Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution." Micromachines 12, no. 11 (November 18, 2021): 1418. http://dx.doi.org/10.3390/mi12111418.
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Medical imaging is widely used in medical diagnosis. The low-resolution image caused by high hardware cost and poor imaging technology leads to the loss of relevant features and even fine texture. Obtaining high-quality medical images plays an important role in disease diagnosis. A surge of deep learning approaches has recently demonstrated high-quality reconstruction for medical image super-resolution. In this work, we propose a light-weight wavelet frequency separation attention network for medical image super-resolution (WFSAN). WFSAN is designed with separated-path for wavelet sub-bands to predict the wavelet coefficients, considering that image data characteristics are different in the wavelet domain and spatial domain. In addition, different activation functions are selected to fit the coefficients. Inputs comprise approximate sub-bands and detail sub-bands of low-resolution wavelet coefficients. In the separated-path network, detail sub-bands, which have more sparsity, are trained to enhance high frequency information. An attention extension ghost block is designed to generate the features more efficiently. All results obtained from fusing layers are contracted to reconstruct the approximate and detail wavelet coefficients of the high-resolution image. In the end, the super-resolution results are generated by inverse wavelet transform. Experimental results show that WFSAN has competitive performance against state-of-the-art lightweight medical imaging methods in terms of quality and quantitative metrics.
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Gupta, Puneet. "Chest Diseases Detection based on Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4142–46. http://dx.doi.org/10.22214/ijraset.2021.35941.
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The paper is on our project that is all about detecting 14 different types of chest diseases using x-ray images of chest. It also neglects the apparels and jewelry’s present on human body while performing the x-ray test thus giving us maximum accuracy of diseases detection. The key goal of project is to know the percentage of diseases detection of all the 14 different tests performed on human chest with maximum accuracy. Chest x-ray imaging is a vital screening and medicine tool for many life threating diseases, however because of shortage of radiologists, the screening tool cannot be wont to treat all patients. Deep learning based mostly medical image classifiers are one potential answer. This project runs, uploads, method and generates reports at any given purpose of your time with accuracy.
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Wu, Xiaochang, and Xiaolin Tian. "An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs." Applied Sciences 10, no. 15 (July 22, 2020): 5032. http://dx.doi.org/10.3390/app10155032.
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Medical image segmentation is a classic challenging problem. The segmentation of parts of interest in cardiac medical images is a basic task for cardiac image diagnosis and guided surgery. The effectiveness of cardiac segmentation directly affects subsequent medical applications. Generative adversarial networks have achieved outstanding success in image segmentation compared with classic neural networks by solving the oversegmentation problem. Cardiac X-ray images are prone to weak edges, artifacts, etc. This paper proposes an adaptive generative adversarial network for cardiac segmentation to improve the segmentation rate of X-ray images by generative adversarial networks. The adaptive generative adversarial network consists of three parts: a feature extractor, a discriminator and a selector. In this method, multiple generators are trained in the feature extractor. The discriminator scores the features of different dimensions. The selector selects the appropriate features and adjusts the network for the next iteration. With the help of the discriminator, this method uses multinetwork joint feature extraction to achieve network adaptivity. This method allows features of multiple dimensions to be combined to perform joint training of the network to enhance its generalization ability. The results of cardiac segmentation experiments on X-ray chest radiographs show that this method has higher segmentation accuracy and less overfitting than other methods. In addition, the proposed network is more stable.
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Cheng, Chunhong, Junyan Wang, Hongbo Li, and Liang Wang. "Optimal Atlas Segmentation on CT Images for Diagnosis of Pediatric Mycoplasma Pneumonia." Scientific Programming 2021 (July 28, 2021): 1–8. http://dx.doi.org/10.1155/2021/2586956.
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This work aimed to explore the clinical application value of CT imaging technology based on the optimal Atlas segmentation algorithm (OASA) in the diagnosis of pediatric mycoplasma pneumonia (MP). Eighty-eight children with MP were selected and divided into group A (CT image based on the OASA) and group B (chest X-ray) according to the diagnosis methods. The detection rate, image feature performance, and image quality satisfaction of the two groups of children were compared. The results showed that the detection rate of group A was 97.73% and that of group B was 95.46%, and there was no considerable difference between the two ( P > 0.05). The pleural effusion detection rate of children in group A was evidently superior to that of X-ray group, while the increased bronchovascular shadows’ detection rate was greatly inferior to that of X-ray group ( P < 0.05). Comparison results of nodules’ shadows, patchy shadows, acinar parenchyma shadows, and interstitial infiltration between two groups showed that there was no notable difference ( P > 0.05). CT image quality satisfaction (98.50%) was higher versus X-ray (79.46%) ( P < 0.05). To sum up, CT images based on the OASA can be adopted in the clinical diagnosis of pediatric MP, and CT images were better than chest X-rays.
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Albahli, Saleh, and Waleed Albattah. "Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms." Journal of X-Ray Science and Technology 28, no. 5 (September 19, 2020): 841–50. http://dx.doi.org/10.3233/xst-200720.
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OBJECTIVE: This study aims to employ the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of novel coronavirus (COVID-19) infected disease. METHOD: This study applied transfer learning method to develop deep learning models for detecting COVID-19 disease. Three existing state-of-the-art deep learning models namely, Inception ResNetV2, InceptionNetV3 and NASNetLarge, were selected and fine-tuned to automatically detect and diagnose COVID-19 disease using chest X-ray images. A dataset involving 850 images with the confirmed COVID-19 disease, 500 images of community-acquired (non-COVID-19) pneumonia cases and 915 normal chest X-ray images was used in this study. RESULTS: Among the three models, InceptionNetV3 yielded the best performance with accuracy levels of 98.63% and 99.02% with and without using data augmentation in model training, respectively. All the performed networks tend to overfitting (with high training accuracy) when data augmentation is not used, this is due to the limited amount of image data used for training and validation. CONCLUSION: This study demonstrated that a deep transfer learning is feasible to detect COVID-19 disease automatically from chest X-ray by training the learning model with chest X-ray images mixed with COVID-19 patients, other pneumonia affected patients and people with healthy lungs, which may help doctors more effectively make their clinical decisions. The study also gives an insight to how transfer learning was used to automatically detect the COVID-19 disease. In future studies, as the amount of available dataset increases, different convolution neutral network models could be designed to achieve the goal more efficiently.
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Sarhan, Ahmad. "Run length encoding based wavelet features for COVID-19 detection in X-rays." BJR|Open 3, no. 1 (January 2021): 20200028. http://dx.doi.org/10.1259/bjro.20200028.
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Objectives: Introduced in his paper is a novel approach for the recognition of COVID-19 cases in chest X-rays. Methods: The discrete Wavelet transform (DWT) is employed in the proposed system to obtain highly discriminative features from the input chest X-ray image. The selected features are then classified by a support vector machine (SVM) classifier as either normal or COVID-19 cases. The DWT is well-known for its energy compression power. The proposed system uses the DWT to decompose the chest X-ray image into a group of approximation coefficients that contain a small number of high-energy (high-magnitude) coefficients. The proposed system introduces a novel coefficient selection scheme that employs hard thresholding combined with run-length encoding to extract only high-magnitude Wavelet approximation coefficients. These coefficients are utilized as features symbolizing the chest X-ray input image. After applying zero-padding to unify their lengths, the feature vectors are introduced to a SVM which classifies them as either normal or COVID-19 cases. Results: The proposed system yields promising results in terms of classification accuracy, which justifies further work in this direction. Conclusion: The DWT can produce a few features that are highly discriminative. By reducing the dimensionality of the feature space, the proposed system is able to reduce the number of required training images and diminish the space and time complexities of the system. Advances in knowledge: Exploiting and reshaping the approximation coefficients can produce discriminative features representing the input image.
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Hasegawa, Jun-Ichi, Noritake Okada, and Jun-Ichiro Toriwaki. "Intelligent retrieval of chest X-ray image database using sketches." Systems and Computers in Japan 20, no. 7 (1989): 29–42. http://dx.doi.org/10.1002/scj.4690200704.
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Hirano, Hokuto, Kazuki Koga, and Kazuhiro Takemoto. "Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks." PLOS ONE 15, no. 12 (December 17, 2020): e0243963. http://dx.doi.org/10.1371/journal.pone.0243963.
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Owing the epidemic of the novel coronavirus disease 2019 (COVID-19), chest X-ray computed tomography imaging is being used for effectively screening COVID-19 patients. The development of computer-aided systems based on deep neural networks (DNNs) has become an advanced open source to rapidly and accurately detect COVID-19 cases because the need for expert radiologists, who are limited in number, forms a bottleneck for screening. However, thus far, the vulnerability of DNN-based systems has been poorly evaluated, although realistic and high-risk attacks using universal adversarial perturbation (UAP), a single (input image agnostic) perturbation that can induce DNN failure in most classification tasks, are available. Thus, we focus on representative DNN models for detecting COVID-19 cases from chest X-ray images and evaluate their vulnerability to UAPs. We consider non-targeted UAPs, which cause a task failure, resulting in an input being assigned an incorrect label, and targeted UAPs, which cause the DNN to classify an input into a specific class. The results demonstrate that the models are vulnerable to non-targeted and targeted UAPs, even in the case of small UAPs. In particular, the 2% norm of the UAPs to the average norm of an image in the image dataset achieves >85% and >90% success rates for the non-targeted and targeted attacks, respectively. Owing to the non-targeted UAPs, the DNN models judge most chest X-ray images as COVID-19 cases. The targeted UAPs allow the DNN models to classify most chest X-ray images into a specified target class. The results indicate that careful consideration is required in practical applications of DNNs to COVID-19 diagnosis; in particular, they emphasize the need for strategies to address security concerns. As an example, we show that iterative fine-tuning of DNN models using UAPs improves the robustness of DNN models against UAPs.
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Min, Young-Kee, Sora Baek, Eun Kyoung Kang, and Seung-Joo Nam. "Characteristics of Patients With Esophageal Dysphagia Assessed by Chest X-Ray Imaging After Videofluoroscopic Swallowing Study." Annals of Rehabilitation Medicine 44, no. 1 (February 29, 2020): 38–47. http://dx.doi.org/10.5535/arm.2020.44.1.38.
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Objective To evaluate the prevalence rate, types, characteristics, and associated factors of esophageal dysphagia detected on chest X-ray images after videofluoroscopic swallowing study (VFSS).Methods The medical records of 535 adults were reviewed retrospectively. Chest X-ray images taken after barium swallow study were analyzed and presence of any residual barium in the esophagus was considered as esophageal dysphagia. Esophageal dysphagia was classified based on the largest width of barium deposit (mild, <2 cm; severe ≥2 cm) and the anatomic level at which it was located (upper and lower esophagus).Results Esophageal residual barium on chest X-ray images was identified in 40 patients (7.5%, 40/535). Esophageal dysphagia was more frequent in individuals aged 65–79 years (odds ratio=4.78, p<0.05) than in those aged <65 years. Mild esophageal dysphagia was more frequent (n=32) than its severe form (n=8). Lower esophageal dysphagia was more frequent (n=31) than upper esophageal dysphagia (n=9). Esophageal residual barium in patients diagnosed with esophageal cancer or lung cancer was significantly associated with severe esophageal dysphagia (p<0.05) and at the upper esophagus level (p<0.01).Conclusion Esophageal residual barium was observed on chest X-ray imaging after VFSS. Esophageal barium in the upper esophagus with a diameter of ≥2 cm is an important indicator of malignancy, and chest X-ray image taken after VFSS is an important step to evaluate the presence of esophageal disorder.
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Zhang, Alan. "Covid-19 Chest X-ray Images: Lung Segmentation and Diagnosis using Neural Networks." International Journal on Computational Science & Applications 10, no. 5 (October 30, 2020): 1–11. http://dx.doi.org/10.5121/ijcsa.2020.10501.
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COVID-19 has caused world-wide disturbances and the machine learning community has been finding ways to combat the disease. Applications of neural networks in image processing tasks allow COVID-19 Chest X-ray images to be meaningfully processed. In this study, the V7 Darwin COVID-19 Chest X-ray Dataset is used to train a U-Net based network that performs lung-region segmentation and a convolutional neural network that performs diagnosis on Chest X-ray images. This dataset is larger than most of the datasets used to develop existing COVID-19 related neural networks. The lung segmentation network achieved an accuracy of 0.9697 on the training set and an accuracy of 0.9575, an Intersectionover-union of 0.8666, and a dice coefficient of 0.9273 on the validation set. The diagnosis network achieved an accuracy of 0.9620 on the training set and an accuracy of 0.9666 and AUC of 0.985 on the validation set.
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Abedalla, Ayat, Malak Abdullah, Mahmoud Al-Ayyoub, and Elhadj Benkhelifa. "Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures." PeerJ Computer Science 7 (June 29, 2021): e607. http://dx.doi.org/10.7717/peerj-cs.607.
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Medical imaging refers to visualization techniques to provide valuable information about the internal structures of the human body for clinical applications, diagnosis, treatment, and scientific research. Segmentation is one of the primary methods for analyzing and processing medical images, which helps doctors diagnose accurately by providing detailed information on the body’s required part. However, segmenting medical images faces several challenges, such as requiring trained medical experts and being time-consuming and error-prone. Thus, it appears necessary for an automatic medical image segmentation system. Deep learning algorithms have recently shown outstanding performance for segmentation tasks, especially semantic segmentation networks that provide pixel-level image understanding. By introducing the first fully convolutional network (FCN) for semantic image segmentation, several segmentation networks have been proposed on its basis. One of the state-of-the-art convolutional networks in the medical image field is U-Net. This paper presents a novel end-to-end semantic segmentation model, named Ens4B-UNet, for medical images that ensembles four U-Net architectures with pre-trained backbone networks. Ens4B-UNet utilizes U-Net’s success with several significant improvements by adapting powerful and robust convolutional neural networks (CNNs) as backbones for U-Nets encoders and using the nearest-neighbor up-sampling in the decoders. Ens4B-UNet is designed based on the weighted average ensemble of four encoder-decoder segmentation models. The backbone networks of all ensembled models are pre-trained on the ImageNet dataset to exploit the benefit of transfer learning. For improving our models, we apply several techniques for training and predicting, including stochastic weight averaging (SWA), data augmentation, test-time augmentation (TTA), and different types of optimal thresholds. We evaluate and test our models on the 2019 Pneumothorax Challenge dataset, which contains 12,047 training images with 12,954 masks and 3,205 test images. Our proposed segmentation network achieves a 0.8608 mean Dice similarity coefficient (DSC) on the test set, which is among the top one-percent systems in the Kaggle competition.
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Masadeh, Mahmoud, Ayah Masadeh, Omar Alshorman, Falak H. Khasawneh, and Mahmoud Ali Masadeh. "An efficient machine learning-based COVID-19 identification utilizing chest X-ray images." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (March 1, 2022): 356. http://dx.doi.org/10.11591/ijai.v11.i1.pp356-366.
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There is no well-known vaccine for coronavirus disease (COVID-19) with 100% efficiency. COVID-19 patients suffer from a lung infection, where lung-related problems can be effectively diagnosed with image techniques. The golden test for COVID-19 diagnosis is the RT-PCR test, which is costly, time-consuming and unavailable for various countries. Thus, machine learning-based tools are a viable solution. Here, we used a labelled chest X-ray of three categories, then performed data cleaning and augmentation to use the data in deep learning-based convolutional neural network (CNN) models. We compared the performance of different models that we gradually built and analyzed their accuracy. For that, we used 2905 chest X-ray scan samples. We were able to develop a model with the best accuracy of 97.44% for identifying COVID-19 using X-ray images. Thus, in this paper, we attested the feasibility of efficiently applying machine learning (ML) based models for medical image classification.
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Park, Junghoon, Il-Youp Kwak, and Changwon Lim. "A Deep Learning Model with Self-Supervised Learning and Attention Mechanism for COVID-19 Diagnosis Using Chest X-ray Images." Electronics 10, no. 16 (August 18, 2021): 1996. http://dx.doi.org/10.3390/electronics10161996.
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The SARS-CoV-2 virus has spread worldwide, and the World Health Organization has declared COVID-19 pandemic, proclaiming that the entire world must overcome it together. The chest X-ray and computed tomography datasets of individuals with COVID-19 remain limited, which can cause lower performance of deep learning model. In this study, we developed a model for the diagnosis of COVID-19 by solving the classification problem using a self-supervised learning technique with a convolution attention module. Self-supervised learning using a U-shaped convolutional neural network model combined with a convolution block attention module (CBAM) using over 100,000 chest X-Ray images with structure similarity (SSIM) index captures image representations extremely well. The system we proposed consists of fine-tuning the weights of the encoder after a self-supervised learning pretext task, interpreting the chest X-ray representation in the encoder using convolutional layers, and diagnosing the chest X-ray image as the classification model. Additionally, considering the CBAM further improves the averaged accuracy of 98.6%, thereby outperforming the baseline model (97.8%) by 0.8%. The proposed model classifies the three classes of normal, pneumonia, and COVID-19 extremely accurately, along with other metrics such as specificity and sensitivity that are similar to accuracy. The average area under the curve (AUC) is 0.994 in the COVID-19 class, indicating that our proposed model exhibits outstanding classification performance.
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Mittal, Ansh, Deepika Kumar, Mamta Mittal, Tanzila Saba, Ibrahim Abunadi, Amjad Rehman, and Sudipta Roy. "Detecting Pneumonia Using Convolutions and Dynamic Capsule Routing for Chest X-ray Images." Sensors 20, no. 4 (February 15, 2020): 1068. http://dx.doi.org/10.3390/s20041068.
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An entity’s existence in an image can be depicted by the activity instantiation vector from a group of neurons (called capsule). Recently, multi-layered capsules, called CapsNet, have proven to be state-of-the-art for image classification tasks. This research utilizes the prowess of this algorithm to detect pneumonia from chest X-ray (CXR) images. Here, an entity in the CXR image can help determine if the patient (whose CXR is used) is suffering from pneumonia or not. A simple model of capsules (also known as Simple CapsNet) has provided results comparable to best Deep Learning models that had been used earlier. Subsequently, a combination of convolutions and capsules is used to obtain two models that outperform all models previously proposed. These models—Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)—detect pneumonia with a test accuracy of 95.33% and 95.90%, respectively. The latter model is studied in detail to obtain a variant called EnCC, where n = 3, 4, 8, 16. Here, the E4CC model works optimally and gives test accuracy of 96.36%. All these models had been trained, validated, and tested on 5857 images from Mendeley.
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Monday, Happy Nkanta, Jianping Li, Grace Ugochi Nneji, Saifun Nahar, Md Altab Hossin, Jehoiada Jackson, and Chukwuebuka Joseph Ejiyi. "COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network." Diagnostics 12, no. 3 (March 18, 2022): 741. http://dx.doi.org/10.3390/diagnostics12030741.
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Chest X-ray (CXR) is becoming a useful method in the evaluation of coronavirus disease 19 (COVID-19). Despite the global spread of COVID-19, utilizing a computer-aided diagnosis approach for COVID-19 classification based on CXR images could significantly reduce the clinician burden. There is no doubt that low resolution, noise and irrelevant annotations in chest X-ray images are a major constraint to the performance of AI-based COVID-19 diagnosis. While a few studies have made huge progress, they underestimate these bottlenecks. In this study, we propose a super-resolution-based Siamese wavelet multi-resolution convolutional neural network called COVID-SRWCNN for COVID-19 classification using chest X-ray images. Concretely, we first reconstruct high-resolution (HR) counterparts from low-resolution (LR) CXR images in order to enhance the quality of the dataset for improved performance of our model by proposing a novel enhanced fast super-resolution convolutional neural network (EFSRCNN) to capture texture details in each given chest X-ray image. Exploiting a mutual learning approach, the HR images are passed to the proposed Siamese wavelet multi-resolution convolutional neural network to learn the high-level features for COVID-19 classification. We validate the proposed COVID-SRWCNN model on public-source datasets, achieving accuracy of 98.98%. Our screening technique achieves 98.96% AUC, 99.78% sensitivity, 98.53% precision, and 98.86% specificity. Owing to the fact that COVID-19 chest X-ray datasets are low in quality, experimental results show that our proposed algorithm obtains up-to-date performance that is useful for COVID-19 screening.
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Babar, Zaheer, Twan van Laarhoven, and Elena Marchiori. "Encoder-decoder models for chest X-ray report generation perform no better than unconditioned baselines." PLOS ONE 16, no. 11 (November 29, 2021): e0259639. http://dx.doi.org/10.1371/journal.pone.0259639.
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High quality radiology reporting of chest X-ray images is of core importance for high-quality patient diagnosis and care. Automatically generated reports can assist radiologists by reducing their workload and even may prevent errors. Machine Learning (ML) models for this task take an X-ray image as input and output a sequence of words. In this work, we show that ML models for this task based on the popular encoder-decoder approach, like ‘Show, Attend and Tell’ (SA&T) have similar or worse performance than models that do not use the input image, called unconditioned baseline. An unconditioned model achieved diagnostic accuracy of 0.91 on the IU chest X-ray dataset, and significantly outperformed SA&T (0.877) and other popular ML models (p-value < 0.001). This unconditioned model also outperformed SA&T and similar ML methods on the BLEU-4 and METEOR metrics. Also, an unconditioned version of SA&T obtained by permuting the reports generated from images of the test set, achieved diagnostic accuracy of 0.862, comparable to that of SA&T (p-value ≥ 0.05).
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Sangulagi, Prashant, and Abhinav Kumar. "Detection of Covid-19 from Chest X-Ray Images." Journal of Scientific Research 66, no. 02 (2022): 172–78. http://dx.doi.org/10.37398/jsr.2022.660223.
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The capability of deep learning on chest radiographs is diagnosed in this dissertation, and an image classifier based on the COVID-Net has been provided to categorize chest X-Ray pictures. Information augmentation was recommended to extend COVID-19 reports 17 times in the case of a little amount of COVID-19 metadata. The system aims to improve transfer learning and model integration by categorizing chest X-ray pictures into three categories: normal, COVID-19, and infectious pneumonia. Choose the models ResNet-101 and ResNet-152 with best outcomes for fusion based on accuracy and loss value, and apathetically enhance its weight percentage during the training procedure. The system can obtain 97 percent accuracy in chest X-Ray pictures post training. In the diagnosing of lung nodules, this technique offers a higher accuracy than radiologist. It can assist radiologists enhance their efficiency levels and diagnostic performance as an additional diagnostic technique.
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Abdi, Ahmed Jibril, Bo Mussmann, Alistair Mackenzie, Oke Gerke, Gitte Maria Jørgensen, Thor Eriksen Bechsgaard, Janni Jensen, Lone Brunshøj Olsen, and Poul Erik Andersen. "Visual Evaluation of Image Quality of a Low Dose 2D/3D Slot Scanner Imaging System Compared to Two Conventional Digital Radiography X-ray Imaging Systems." Diagnostics 11, no. 10 (October 19, 2021): 1932. http://dx.doi.org/10.3390/diagnostics11101932.
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The purpose of this study was to assess the image quality of the low dose 2D/3D slot scanner (LDSS) imaging system compared to conventional digital radiography (DR) imaging systems. Visual image quality was assessed using the visual grading analysis (VGA) method. This method is a subjective approach that uses a human observer to evaluate and optimise radiographic images for different imaging technologies. Methods and materials: ten posterior-anterior (PA) and ten lateral (LAT) images of a chest anthropomorphic phantoms and a knee phantom were acquired by an LDSS imaging system and two conventional DR imaging systems. The images were shown in random order to three (chest) radiologists and three experienced (knee) radiographers, who scored the images against a number of criteria. Inter- and intraobserver agreement was assessed using Fleiss’ kappa and weighted kappa. Results: the statistical comparison of the agreement between the observers showed good interobserver agreement, with Fleiss’ kappa coefficients of 0.27–0.63 and 0.23–0.45 for the chest and knee protocols, respectively. Comparison of intraobserver agreement also showed good agreement with weighted kappa coefficients of 0.27–0.63 and 0.23–0.45 for the chest and knee protocols, respectively. The LDSS imaging system achieved significantly higher VGA image quality compared to the DR imaging systems in the AP and LAT chest protocols (p < 0.001). However, the LDSS imaging system achieved lower image quality than one DR system (p ≤ 0.016) and equivalent image quality to the other DR systems (p ≤ 0.27) in the knee protocol. The LDSS imaging system achieved effective dose savings of 33–52% for the chest protocol and 30–35% for the knee protocol compared with DR systems. Conclusions: this work has shown that the LDSS imaging system has the potential to acquire chest and knee images at diagnostic quality and at a lower effective dose than DR systems.
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Sun, Mark GF, Senjuti Saha, Syed Ahmar Shah, Saturnino Luz, Harish Nair, and Samir Saha. "Study protocol and design for the assessment of paediatric pneumonia from X-ray images using deep learning." BMJ Open 11, no. 4 (April 2021): e044461. http://dx.doi.org/10.1136/bmjopen-2020-044461.
Full textAbstract:
IntroductionIn low-income and middle-income countries, pneumonia remains the leading cause of illness and death in children<5 years. The recommended tool for diagnosing paediatric pneumonia is the interpretation of chest X-ray images, which is difficult to standardise and requires trained clinicians/radiologists. Current automated computational tools have primarily focused on assessing adult pneumonia and were trained on images evaluated by a single specialist. We aim to provide a computational tool using a deep-learning approach to diagnose paediatric pneumonia using X-ray images assessed by multiple specialists trained by the WHO expert X-ray image reading panel.Methods and analysisApproximately 10 000 paediatric chest X-ray images are currently being collected from an ongoing WHO-supported surveillance study in Bangladesh. Each image will be read by two trained clinicians/radiologists for the presence or absence of primary endpoint pneumonia (PEP) in each lung, as defined by the WHO. Images whose PEP labels are discordant in either lung will be reviewed by a third specialist and the final assignment will be made using a majority vote. Convolutional neural networks will be used for lung segmentation to align and scale the images to a reference, and for interpretation of the images for the presence of PEP. The model will be evaluated against an independently collected and labelled set of images from the WHO. The study outcome will be an automated method for the interpretation of chest radiographs for diagnosing paediatric pneumonia.Ethics and disseminationAll study protocols were approved by the Ethical Review Committees of the Bangladesh Institute of Child Health, Bangladesh. The study sponsor deemed it unnecessary to attain ethical approval from the Academic and Clinical Central Office for Research and Development of University of Edinburgh, UK. The study uses existing X-ray images from an ongoing WHO-coordinated surveillance. All findings will be published in an open-access journal. All X-ray labels and statistical code will be made openly available. The model and images will be made available on request.
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Daya, Rupesh Baloo, Maurice A. Kibel, Richard Denys Pitcher, Lesley Workman, Tania S. Douglas, and Virginia Sanders. "A pilot study evaluating erect chest imaging in children, using the Lodox Statscan digital X-ray machine." South African Journal of Radiology 13, no. 4 (November 30, 2009): 80. http://dx.doi.org/10.4102/sajr.v13i4.485.
Full textAbstract:
ABSTRACT
Background:
Chest radiography accounts for a significant proportion of ionising radiation in children. The radiation dose of radiographs performed on the Lodox Statscan system has been shown to be lower than that of a computed radiography (CR) system. The role of the Lodox Statscan (hereafter referred to as the Statscan) in routine erect chest radiography in children has not been evaluated.
Objective:
To evaluate the image quality and diagnostic accuracy of erect paediatric chest radiographs obtained with the Statscan and compare this with conventional erect chest images obtained with a CR system.
Materials and Methods:
Thirty three children with suspected chest pathology were enrolled randomly over a period of three months. Erect chest radiographs were obtained with the Statscan, and a Shimadzu R-20J X-ray machine coupled with a Fuji FCR 5000 CR system. Image quality and diagnostic accuracy and diagnostic capability were evaluated between the two modalities.
Results:
The erect Statscan allowed superior visualisation of the three major airways. Statscan images however, demonstrated exposure and movement artifacts with hemidiaphragms and ribs most prone to movement. Bronchovascular clarity was also considered unsatisfactory on the Statscan images.
Conclusion:
The Statscan has limitations in erect chest radiography in terms of movement artefacts, exposure fluctuations, and poor definition of lung markings. Despite this, the Statscan allows better visualisation of the major airways, equivalent to a ‘high KV’ film at a fraction of the radiation dose. This supports the finding of an earlier study evaluating Statscan images in trauma cases, where the images were taken supine. Statscan has great potential in assisting in the diagnosis of childhood tuberculosis where airway narrowing occurs as a result of nodal compression.
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Senthil Kumar, J., S. Appavu Alias Balamurugan, and S. Sasikala. "A Novel Tuberculosis Prediction Model by Extracting Radiological Features Present in Chest X-ray Images Using Modified Discrete Grey Wolf Optimizer Based Segmentation." Journal of Medical Imaging and Health Informatics 11, no. 10 (October 1, 2021): 2519–28. http://dx.doi.org/10.1166/jmihi.2021.3837.
Full textAbstract:
In 2018, an invariant numbers ranging from 10 million people suffered from Tuberculosis (TB) approximately that has remained quite stable in recent years, based on the WHO 2019 survey report. This infection rate differs invariable among countries, from less than 5 to more than 500 new infections per 1,00,000 people each year, with a global average of around 130. Around 1.2 million HIV negative deaths existed in 2018. If this prevailing disease were diagnosed earlier, the death rate would have been under control, however sophisticated testing techniques tend to be cost prohibitive of wider acceptance. Some of the most important methods for TB diagnosis include thoracic X-ray image interpretation through image processing by the identification of various structures on thoracic X-rays and anomaly assessment is an important stage in computer-aided diagnosis systems. Chest form and size may contain indications for serious disorders such as pneumothorax, pneumoconiosis, tuberculosis and emphysema. Substantial work might have contributed to simplify diagnosis through implementing various statistical strategies to medical images, minimizing overtime and dramatically lowering overhead costs. In addition, recent advances in deep learning have provided magnificent results in the detection of images in different fields, but their use in diagnose TB remains limited. Thus, this work focuses on the development of a novel approach in disease detection. The concepts presented in this work are placed into practice and linked to current literature. We also proposed an automatic approach in conventional poster anterior chest X-rays for TB identification and diagnosis. We use the chest X-ray image with modified discrete grey wolf optimizer for segmentation techniques to eradicate abnormal areas and shape abnormality. We extract various features from the X-ray image with a shear let extraction that allows the image to be classified as normal or abnormal, based on a deep learning classifier, via the improved residual VGG net CNN with big data. Using Shenzhen Hospital Chest X-ray data set we test the efficiency of our system. The suggested technique has competitive results with comparatively shorter training period and greater precision depending on Masientropy based discrete gray wolf optimizer segmentation with an improved residual VGG net CNN. All the simulations are carried out in a mat lab environment.
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Polat, Çağín, Onur Karaman, Ceren Karaman, Güney Korkmaz, Mehmet Can Balcı, and Sevim Ercan Kelek. "COVID-19 diagnosis from chest X-ray images using transfer learning: Enhanced performance by debiasing dataloader." Journal of X-Ray Science and Technology 29, no. 1 (February 19, 2021): 19–36. http://dx.doi.org/10.3233/xst-200757.
Full textAbstract:
BACKGROUND: Chest X-ray imaging has been proved as a powerful diagnostic method to detect and diagnose COVID-19 cases due to its easy accessibility, lower cost and rapid imaging time. OBJECTIVE: This study aims to improve efficacy of screening COVID-19 infected patients using chest X-ray images with the help of a developed deep convolutional neural network model (CNN) entitled nCoV-NET. METHODS: To train and to evaluate the performance of the developed model, three datasets were collected from resources of “ChestX-ray14”, “COVID-19 image data collection”, and “Chest X-ray collection from Indiana University,” respectively. Overall, 299 COVID-19 pneumonia cases and 1,522 non-COVID 19 cases are involved in this study. To overcome the probable bias due to the unbalanced cases in two classes of the datasets, ResNet, DenseNet, and VGG architectures were re-trained in the fine-tuning stage of the process to distinguish COVID-19 classes using a transfer learning method. Lastly, the optimized final nCoV-NET model was applied to the testing dataset to verify the performance of the proposed model. RESULTS: Although the performance parameters of all re-trained architectures were determined close to each other, the final nCOV-NET model optimized by using DenseNet-161 architecture in the transfer learning stage exhibits the highest performance for classification of COVID-19 cases with the accuracy of 97.1 %. The Activation Mapping method was used to create activation maps that highlights the crucial areas of the radiograph to improve causality and intelligibility. CONCLUSION: This study demonstrated that the proposed CNN model called nCoV-NET can be utilized for reliably detecting COVID-19 cases using chest X-ray images to accelerate the triaging and save critical time for disease control as well as assisting the radiologist to validate their initial diagnosis.