{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T04:28:14Z","timestamp":1726460894849},"reference-count":140,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1016\/j.asoc.2022.109319","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T12:22:57Z","timestamp":1658146977000},"page":"109319","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":45,"special_numbering":"C","title":["Chest X-ray analysis empowered with deep learning: A systematic review"],"prefix":"10.1016","volume":"126","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-4520-3819","authenticated-orcid":false,"given":"Dulani","family":"Meedeniya","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5926-2169","authenticated-orcid":false,"given":"Hashara","family":"Kumarasinghe","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7957-0345","authenticated-orcid":false,"given":"Shammi","family":"Kolonne","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1243-1567","authenticated-orcid":false,"given":"Chamodi","family":"Fernando","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3134-7720","authenticated-orcid":false,"given":"Isabel De la Torre","family":"D\u00edez","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5834-6571","authenticated-orcid":false,"given":"Gon\u00e7alo","family":"Marques","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"year":"2019","series-title":"Pneumonia","author":"World Health Organization","key":"10.1016\/j.asoc.2022.109319_b1"},{"key":"10.1016\/j.asoc.2022.109319_b2","series-title":"Disease Control Priorities in Developing Countries","first-page":"483","article-title":"Acute respiratory infections in children","author":"Simoes","year":"2006"},{"issue":"128","key":"10.1016\/j.asoc.2022.109319_b3","first-page":"1","article-title":"COVID-19: immunopathogenesis and immunotherapeutics","volume":"5","author":"Yang","year":"2020","journal-title":"Signal Transduct. Target. Therapy"},{"year":"2021","series-title":"Pneumonia","author":"Radiologyinfo.org","key":"10.1016\/j.asoc.2022.109319_b4"},{"issue":"1","key":"10.1016\/j.asoc.2022.109319_b5","doi-asserted-by":"crossref","DOI":"10.1148\/ryct.2020200028","article-title":"Chest imaging appearance of COVID-19 infection","volume":"2","author":"Kong","year":"2020","journal-title":"Radiol. Cardiothoracic Imaging"},{"key":"10.1016\/j.asoc.2022.109319_b6","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.compmedimag.2019.05.005","article-title":"SDFN: Segmentation-based deep fusion network for thoracic disease classification in chest X-ray images","volume":"75","author":"Liu","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"issue":"12","key":"10.1016\/j.asoc.2022.109319_b7","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1109\/42.974918","article-title":"Computer-aided diagnosis in chest radiography: a survey","volume":"20","author":"Ginneken","year":"2001","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2022.109319_b8","doi-asserted-by":"crossref","first-page":"43168","DOI":"10.1109\/ACCESS.2021.3065965","article-title":"Glioma survival analysis empowered with data engineering - A survey","volume":"9","author":"Wijethilake","year":"2021","journal-title":"IEEE Access"},{"issue":"2","key":"10.1016\/j.asoc.2022.109319_b9","doi-asserted-by":"crossref","DOI":"10.3390\/app10020559","article-title":"A novel transfer learning based approach for pneumonia detection in chest X-ray images","volume":"10","author":"Chouhan","year":"2020","journal-title":"Appl. Sci."},{"key":"10.1016\/j.asoc.2022.109319_b10","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s13246-020-00865-4","article-title":"Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks","volume":"43","author":"Apostolopoulos","year":"2020","journal-title":"Phys. Eng. Sci. Med."},{"key":"10.1016\/j.asoc.2022.109319_b11","series-title":"Deep Learning Techniques for Biomedical and Health Informatics","first-page":"305","article-title":"Automated neuroscience decision support framework","author":"Rubasinghe","year":"2020"},{"issue":"6","key":"10.1016\/j.asoc.2022.109319_b12","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1007\/s11633-020-1231-6","article-title":"Integration of facial thermography in eeg-based classification of ASD","volume":"17","author":"Haputhanthri","year":"2020","journal-title":"International Journal of Automation and Computing (IJAC)"},{"issue":"12","key":"10.1016\/j.asoc.2022.109319_b13","doi-asserted-by":"crossref","DOI":"10.3390\/jimaging6120131","article-title":"A survey of deep learning for lung disease detection on medical images: State-of-the-art, taxonomy, issues and future directions","volume":"6","author":"Kieu","year":"2020","journal-title":"J. Imaging"},{"key":"10.1016\/j.asoc.2022.109319_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102125","article-title":"Deep learning for chest X-ray analysis: A survey","volume":"72","author":"\u00c7all\u0131","year":"2021","journal-title":"Med. Image Anal."},{"issue":"7","key":"10.1016\/j.asoc.2022.109319_b15","doi-asserted-by":"crossref","first-page":"890","DOI":"10.30534\/ijeter\/2021\/09972021","article-title":"A survey on pneumonia detection methods using computer-aided diagnosis","volume":"9","author":"Jain","year":"2021","journal-title":"Int. J. Emerg. Trends Eng. Res."},{"key":"10.1016\/j.asoc.2022.109319_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.scs.2020.102589","article-title":"Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey","volume":"65","author":"Bhattacharya","year":"2021","journal-title":"Sustainable Cities Soc."},{"issue":"4","key":"10.1016\/j.asoc.2022.109319_b17","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MCI.2020.3019873","article-title":"Computational intelligence techniques for combating COVID-19: A survey","volume":"15","author":"Tseng","year":"2020","journal-title":"IEEE Comput. Intell. Mag."},{"key":"10.1016\/j.asoc.2022.109319_b18","series-title":"2020 International Conference on Emerging Trends in Information Technology and Engineering (Ic-ETITE)","first-page":"1","article-title":"Pneumonia detection using deep learning approaches","author":"Tilve","year":"2020"},{"key":"10.1016\/j.asoc.2022.109319_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103898","article-title":"Accuracy of deep learning for automated detection of pneumonia using chest X-Ray images: A systematic review and meta-analysis","volume":"123","author":"Li","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2022.109319_b20","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1097\/01.NAJ.0000444496.24228.2c","article-title":"The systematic review: An overview","volume":"114","author":"Aromataris","year":"2014","journal-title":"AJN Am. J. Nursing"},{"key":"10.1016\/j.asoc.2022.109319_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2020.110337","article-title":"A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic","volume":"141","author":"Rasheed","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"10.1016\/j.asoc.2022.109319_b22","series-title":"Emerging Technology Trends in Electronics, Communication and Networking","first-page":"254","article-title":"Convolutional neural network based chest X-Ray image classification for pneumonia diagnosis","author":"Bhatt","year":"2020"},{"key":"10.1016\/j.asoc.2022.109319_b23","series-title":"2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society","first-page":"2186","article-title":"Classifying pneumonia among chest X-Rays using transfer learning","author":"Irfan","year":"2020"},{"issue":"6","key":"10.1016\/j.asoc.2022.109319_b24","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics10060417","article-title":"Efficient pneumonia detection in chest xray images using deep transfer learning","volume":"10","author":"Hashmi","year":"2020","journal-title":"Diagnostics"},{"issue":"7","key":"10.1016\/j.asoc.2022.109319_b25","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann. Internal Med."},{"key":"10.1016\/j.asoc.2022.109319_b26","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.asoc.2022.109319_b27","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2261","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"year":"2016","series-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \u00a10.5MB model size","author":"Iandola","key":"10.1016\/j.asoc.2022.109319_b28"},{"year":"2014","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","key":"10.1016\/j.asoc.2022.109319_b29"},{"year":"2017","series-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","key":"10.1016\/j.asoc.2022.109319_b30"},{"key":"10.1016\/j.asoc.2022.109319_b31","doi-asserted-by":"crossref","unstructured":"F. Chollet, Xception: Deep Learning with Depthwise Separable Convolutions, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2017, pp. 1800\u20131807, http:\/\/dx.doi.org\/10.1109\/CVPR.2017.195.","DOI":"10.1109\/CVPR.2017.195"},{"key":"10.1016\/j.asoc.2022.109319_b32","series-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","first-page":"3859","article-title":"Dynamic routing between capsules","author":"Sabour","year":"2017"},{"key":"10.1016\/j.asoc.2022.109319_b33","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.asoc.2022.109319_b34","series-title":"Proceedings of the 36th International Conference on Machine Learning","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume":"vol. 97","author":"Tan","year":"2019"},{"key":"10.1016\/j.asoc.2022.109319_b35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compmedimag.2017.04.001","article-title":"Recent developments in machine learning for medical imaging applications","volume":"57","author":"Wong","year":"2017","journal-title":"Comput. Med. Imaging Graph."},{"year":"2021","series-title":"Deep Learning with Python","author":"Chollet","key":"10.1016\/j.asoc.2022.109319_b36"},{"key":"10.1016\/j.asoc.2022.109319_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106580","article-title":"A novel medical diagnosis model for COVID-19 infection detection based on deep features and Bayesian optimization","volume":"97","author":"Nour","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2022.109319_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106691","article-title":"Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network","volume":"96","author":"Marques","year":"2020","journal-title":"Appl. Soft Comput."},{"year":"2020","series-title":"Transfer Learning","author":"Yang","key":"10.1016\/j.asoc.2022.109319_b39"},{"key":"10.1016\/j.asoc.2022.109319_b40","series-title":"2020 International Electronics Symposium","first-page":"476","article-title":"Convolutional neural network for automatic pneumonia detection in chest radiography","author":"Khoiriyah","year":"2020"},{"key":"10.1016\/j.asoc.2022.109319_b41","series-title":"2020 International Conference on Smart Electronics and Communication","first-page":"589","article-title":"Deep learning based chest X-Ray image as a diagnostic tool for COVID-19","author":"Padma","year":"2020"},{"issue":"2","key":"10.1016\/j.asoc.2022.109319_b42","article-title":"A deep learning approach for COVID-19 8 viral pneumonia screening with X-Ray images","volume":"2","author":"Ahmed","year":"2021","journal-title":"Digit. Gov.: Res. Pract."},{"key":"10.1016\/j.asoc.2022.109319_b43","series-title":"2018 10th International Conference on Knowledge and Systems Engineering","first-page":"300","article-title":"Applying multi-CNNs model for detecting abnormal problem on chest x-ray images","author":"Kieu","year":"2018"},{"key":"10.1016\/j.asoc.2022.109319_b44","article-title":"COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images","volume":"10","author":"Wang","year":"2020","journal-title":"Sci. Rep."},{"year":"2020","series-title":"Darwin\u2019s neural network: AI-based strategies for rapid and scalable cell and coronavirus screening","author":"Lee","key":"10.1016\/j.asoc.2022.109319_b45"},{"key":"10.1016\/j.asoc.2022.109319_b46","doi-asserted-by":"crossref","unstructured":"J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database, in: CVPR09, 2009.","DOI":"10.1109\/CVPR.2009.5206848"},{"year":"2020","series-title":"COVID-ResNet: A deep learning framework for screening of COVID19 from radiographs","author":"Farooq","key":"10.1016\/j.asoc.2022.109319_b47"},{"year":"2009","series-title":"Learning Multiple Layers of Features from Tiny Images","author":"Krizhevsky","key":"10.1016\/j.asoc.2022.109319_b48"},{"year":"1998","series-title":"The MNIST DATABASE of handwritten digits","author":"LeCun","key":"10.1016\/j.asoc.2022.109319_b49"},{"key":"10.1016\/j.asoc.2022.109319_b50","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.patcog.2016.07.001","article-title":"Towards better exploiting convolutional neural networks for remote sensing scene classification","volume":"61","author":"Nogueira","year":"2017","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.asoc.2022.109319_b51","series-title":"Advances in Bioinformatics, Multimedia, and Electronics Circuits and Signals","first-page":"155","article-title":"Deep convolutional neural network with transfer learning for detecting pneumonia on chest X-Rays","author":"Chhikara","year":"2020"},{"key":"10.1016\/j.asoc.2022.109319_b52","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2020.109944","article-title":"Application of deep learning for fast detection of COVID-19 in X-Rays using ncovnet","volume":"138","author":"Panwar","year":"2020","journal-title":"Chaos Solitons Fractals"},{"issue":"5","key":"10.1016\/j.asoc.2022.109319_b53","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","article-title":"Convolutional neural networks for medical image analysis: Full training or fine tuning?","volume":"35","author":"Tajbakhsh","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2022.109319_b54","series-title":"2021 8th International Conference on Computing for Sustainable Global Development","first-page":"769","article-title":"Multi class image classification for detection of diseases using chest X ray images","author":"Choudhuri","year":"2021"},{"key":"10.1016\/j.asoc.2022.109319_b55","doi-asserted-by":"crossref","unstructured":"A. Pant, A. Jain, K.C. Nayak, D. Gandhi, B. Prasad, Pneumonia Detection: An Efficient Approach Using Deep Learning, in: 2020 11th International Conference on Computing, Communication and Networking Technologies, ICCCNT, 2020, pp. 1\u20136, http:\/\/dx.doi.org\/10.1109\/ICCCNT49239.2020.9225543.","DOI":"10.1109\/ICCCNT49239.2020.9225543"},{"key":"10.1016\/j.asoc.2022.109319_b56","series-title":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems","first-page":"26","article-title":"The multimodal deep learning for diagnosing COVID-19 pneumonia from chest CT-scan and X-Ray images","author":"Hilmizen","year":"2020"},{"key":"10.1016\/j.asoc.2022.109319_b57","first-page":"1","article-title":"Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network","author":"Das","year":"2021","journal-title":"Pattern Anal. Appl."},{"key":"10.1016\/j.asoc.2022.109319_b58","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/5513679","article-title":"Artificial neural network-based deep learning model for COVID-19 patient detection using X-Ray chest images","volume":"2021","author":"Shorfuzzaman","year":"2021","journal-title":"J. Healthc. Eng."},{"issue":"2","key":"10.1016\/j.asoc.2022.109319_b59","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1002\/ima.22566","article-title":"Convolutional capsule network for COVID-19 detection using radiography images","volume":"31","author":"Tiwari","year":"2021","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"10.1016\/j.asoc.2022.109319_b60","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103805","article-title":"COVID-19 detection using deep learning models to exploit social mimic optimization and structured chest X-ray images using fuzzy color and stacking approaches","volume":"121","author":"To\u011fa\u00e7ar","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2022.109319_b61","article-title":"Evolution of image segmentation using deep convolutional neural network: A survey","volume":"201\u2013202","author":"Sultana","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.asoc.2022.109319_b62","series-title":"Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications","first-page":"113180G","article-title":"Two-stage deep learning architecture for pneumonia detection and its diagnosis in chest radiographs","volume":"vol. 11318","author":"Narayanan","year":"2020"},{"issue":"2","key":"10.1016\/j.asoc.2022.109319_b63","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1109\/TMI.2013.2290491","article-title":"Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration","volume":"33","author":"Candemir","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"10.1016\/j.asoc.2022.109319_b64","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1109\/TMI.2013.2284099","article-title":"Automatic tuberculosis screening using chest radiographs","volume":"33","author":"Jaeger","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"year":"2018","series-title":"Capsules for object segmentation","author":"LaLonde","key":"10.1016\/j.asoc.2022.109319_b65"},{"key":"10.1016\/j.asoc.2022.109319_b66","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","first-page":"664","article-title":"Matwo-CapsNet: A multi-label semantic segmentation capsules network","author":"Bonheur","year":"2019"},{"key":"10.1016\/j.asoc.2022.109319_b67","series-title":"2020 Third International Conference on Vocational Education and Electrical Engineering","first-page":"1","article-title":"Pneumonia and COVID-19 detection using convolutional neural networks","author":"Militante","year":"2020"},{"key":"10.1016\/j.asoc.2022.109319_b68","series-title":"2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society","first-page":"1238","article-title":"Multi-view ensemble convolutional neural network to improve classification of pneumonia in low contrast chest X-Ray images","author":"Ferreira","year":"2020"},{"key":"10.1016\/j.asoc.2022.109319_b69","series-title":"2019 Scientific Meeting on Electrical-Electronics Biomedical Engineering and Computer Science","first-page":"1","article-title":"Diagnosis of pneumonia from chest X-Ray images using deep learning","author":"Ayan","year":"2019"},{"key":"10.1016\/j.asoc.2022.109319_b70","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1007\/s00264-020-04609-7","article-title":"Deep learning COVID-19 detection bias: accuracy through artificial intelligence","volume":"44","author":"Vaid","year":"2020","journal-title":"Int. Orthopaedics"},{"issue":"2","key":"10.1016\/j.asoc.2022.109319_b71","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1007\/s10489-020-01829-7","article-title":"Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network","volume":"51","author":"Abbas","year":"2021","journal-title":"Appl. Intell."},{"key":"10.1016\/j.asoc.2022.109319_b72","article-title":"COVID faster R\u2013CNN: A novel framework to diagnose novel coronavirus disease (COVID-19) in X-Ray images","volume":"20","author":"Shibly","year":"2020","journal-title":"Inf. Med. Unlocked"},{"key":"10.1016\/j.asoc.2022.109319_b73","series-title":"2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science","first-page":"1","article-title":"Classification of chest pneumonia from x-ray images using new architecture based on ResNet","author":"Youssef","year":"2020"},{"year":"2020","series-title":"Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images","author":"Hemdan","key":"10.1016\/j.asoc.2022.109319_b74"},{"issue":"2","key":"10.1016\/j.asoc.2022.109319_b75","article-title":"Optimization and fine-tuning of DenseNet model for classification of COVID-19 cases in medical imaging","volume":"1","author":"Chauhan","year":"2021","journal-title":"Int. J. Inf. Manag. Data Insights"},{"year":"2020","series-title":"The diagnostic evaluation of convolutional neural network (CNN) for the assessment of chest X-ray of patients infected with COVID-19","author":"Bukhari","key":"10.1016\/j.asoc.2022.109319_b76"},{"key":"10.1016\/j.asoc.2022.109319_b77","doi-asserted-by":"crossref","first-page":"81902","DOI":"10.1109\/ACCESS.2021.3086229","article-title":"COVID-19 detection based on image regrouping and resnet-SVM using chest X-Ray images","volume":"9","author":"Zhou","year":"2021","journal-title":"IEEE Access"},{"year":"2020","series-title":"Detection of coronavirus disease (covid-19) based on deep features","author":"Sethy","key":"10.1016\/j.asoc.2022.109319_b78"},{"key":"10.1016\/j.asoc.2022.109319_b79","series-title":"Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, vol. 11600","first-page":"164","article-title":"Deep learning-based detection of COVID-19 from chest x-ray images","author":"Manokaran","year":"2021"},{"key":"10.1016\/j.asoc.2022.109319_b80","series-title":"2020 RIVF International Conference on Computing and Communication Technologies","first-page":"1","article-title":"CNN-based deep learning model for chest X-ray health classification using TensorFlow","author":"Tobias","year":"2020"},{"key":"10.1016\/j.asoc.2022.109319_b81","doi-asserted-by":"crossref","first-page":"37265","DOI":"10.1109\/ACCESS.2020.2974242","article-title":"Learning to recognize chest-xray images faster and more efficiently based on multi-kernel depthwise convolution","volume":"8","author":"Hu","year":"2020","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.asoc.2022.109319_b82","doi-asserted-by":"crossref","first-page":"559","DOI":"10.31661\/jbpe.v0i0.2008-1153","article-title":"Transfer learning-based automatic detection of coronavirus disease 2019 (COVID-19) from chest X-ray images","volume":"10","author":"Mohammadi","year":"2020","journal-title":"J. Biomed. Phys. Eng."},{"key":"10.1016\/j.asoc.2022.109319_b83","series-title":"2020 3rd International Conference on Intelligent Sustainable Systems","first-page":"1032","article-title":"Detection of Covid-19 patients using chest X-ray images with convolution neural network and mobile net","author":"Jabber","year":"2020"},{"key":"10.1016\/j.asoc.2022.109319_b84","doi-asserted-by":"crossref","unstructured":"S. Kolonne, H. Kumarasinghe, C. Fernando, D. Meedeniya, MobileNetV2 Based Chest X-Rays Classification, in: Proceedings of International Conference on Decision Aid Sciences and Application, DASA, Bahrain, 2021, pp. 57\u201361, http:\/\/dx.doi.org\/10.1109\/DASA53625.2021.9682248.","DOI":"10.1109\/DASA53625.2021.9682248"},{"year":"2017","series-title":"CheXNet: Radiologist-level pneumonia detection on chest X-Rays with deep learning","author":"Rajpurkar","key":"10.1016\/j.asoc.2022.109319_b85"},{"key":"10.1016\/j.asoc.2022.109319_b86","series-title":"2019 IEEE International Conference on Electrical, Computer and Communication Technologies","first-page":"1","article-title":"Pneumonia detection using CNN based feature extraction","author":"Varshni","year":"2019"},{"key":"10.1016\/j.asoc.2022.109319_b87","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2020.110122","article-title":"Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks","volume":"140","author":"Toraman","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"10.1016\/j.asoc.2022.109319_b88","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1016\/j.patrec.2020.09.010","article-title":"COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images","volume":"138","author":"Afshar","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.asoc.2022.109319_b89","first-page":"1","article-title":"Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images","author":"Luz","year":"2021","journal-title":"Res. Biomed. Eng."},{"key":"10.1016\/j.asoc.2022.109319_b90","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114883","article-title":"COVID-19: Automatic detection from X-ray images by utilizing deep learning methods","volume":"176","author":"Nigam","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2022.109319_b91","doi-asserted-by":"crossref","DOI":"10.1016\/j.mehy.2020.109761","article-title":"COVIDiagnosis-net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images","volume":"140","author":"Ucar","year":"2020","journal-title":"Med. Hypotheses"},{"key":"10.1016\/j.asoc.2022.109319_b92","series-title":"2020 International Conference on Electrical, Communication, and Computer Engineering","first-page":"1","article-title":"Chest X-Ray abnormality detection based on SqueezeNet","author":"Akpinar","year":"2020"},{"key":"10.1016\/j.asoc.2022.109319_b93","article-title":"COVID-19 deep learning prediction model using publicly available radiologist-adjudicated chest X-ray images as training data: preliminary findings","volume":"2020","author":"Azemin","year":"2020","journal-title":"Int. J. Biomed. Imaging"},{"key":"10.1016\/j.asoc.2022.109319_b94","unstructured":"K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, in: 3rd International Conference on Learning Representations, ICLR, 2015, http:\/\/dx.doi.org\/10.48550\/arXiv.1409.1556."},{"key":"10.1016\/j.asoc.2022.109319_b95","doi-asserted-by":"crossref","unstructured":"C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going Deeper With Convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2015.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10.1016\/j.asoc.2022.109319_b96","doi-asserted-by":"crossref","unstructured":"C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the Inception Architecture for Computer Vision, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2016.","DOI":"10.1109\/CVPR.2016.308"},{"key":"10.1016\/j.asoc.2022.109319_b97","doi-asserted-by":"crossref","unstructured":"M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.-C. Chen, MobileNetV2: Inverted Residuals and Linear Bottlenecks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2018, pp. 4510\u20134520, http:\/\/dx.doi.org\/10.1109\/CVPR.2018.00474.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"10.1016\/j.asoc.2022.109319_b98","doi-asserted-by":"crossref","first-page":"241","DOI":"10.5614\/itbj.ict.res.appl.2019.13.3.5","article-title":"Ultrasound nerve segmentation using deep probabilistic programming","volume":"13","author":"Rubasinghe","year":"2019","journal-title":"J. ICT Res. Appl."},{"key":"10.1016\/j.asoc.2022.109319_b99","series-title":"Medical Imaging 2021: Physics of Medical Imaging, vol. 11595","first-page":"115953I","article-title":"An automatic approach to lung region segmentation in chest x-ray images using adapted U-net architecture","author":"Rahman","year":"2021"},{"year":"2018","series-title":"Large dataset of labeled optical coherence tomography (OCT) and chest X-Ray images","author":"Kermany","key":"10.1016\/j.asoc.2022.109319_b100"},{"key":"10.1016\/j.asoc.2022.109319_b101","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/4180949","article-title":"An efficient deep learning approach to pneumonia classification in healthcare","volume":"2019","author":"Stephen","year":"2019","journal-title":"J. Healthcare Eng."},{"key":"10.1016\/j.asoc.2022.109319_b102","series-title":"Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING","first-page":"112","article-title":"Classification of images of childhood pneumonia using convolutional neural networks","author":"Saraiva","year":"2019"},{"key":"10.1016\/j.asoc.2022.109319_b103","first-page":"1","article-title":"COV19-CNNet and COV19-ResNet: diagnostic inference engines for early detection of COVID-19","author":"Keles","year":"2021","journal-title":"Cogn. Comput."},{"year":"2020","series-title":"COVID-19 image data collection: Prospective predictions are the future","author":"Cohen","key":"10.1016\/j.asoc.2022.109319_b104"},{"key":"10.1016\/j.asoc.2022.109319_b105","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/6621607","article-title":"An efficient CNN model for COVID-19 disease detection based on X-Ray image classification","volume":"2021","author":"Reshi","year":"2021","journal-title":"Complexity"},{"key":"10.1016\/j.asoc.2022.109319_b106","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105581","article-title":"CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images","volume":"196","author":"Khan","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"issue":"6","key":"10.1016\/j.asoc.2022.109319_b107","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1177\/2472630320958376","article-title":"Detection of COVID-19 from chest X-Ray images using convolutional neural networks","volume":"25","author":"Sekeroglu","year":"2020","journal-title":"SLAS Technol.: Transl. Life Sci. Innov."},{"issue":"4","key":"10.1016\/j.asoc.2022.109319_b108","doi-asserted-by":"crossref","first-page":"1480","DOI":"10.3390\/s21041480","article-title":"COVID-19 detection from chest X-ray images using feature fusion and deep learning","volume":"21","author":"Ahsan","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.asoc.2022.109319_b109","doi-asserted-by":"crossref","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","article-title":"Can AI help in screening viral and COVID-19 pneumonia?","volume":"8","author":"Chowdhury","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2022.109319_b110","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104319","article-title":"Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images","volume":"132","author":"Rahman","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2022.109319_b111","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2020.110245","article-title":"CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images","volume":"140","author":"Ouchicha","year":"2020","journal-title":"Chaos Solitons Fractals"},{"year":"2018","series-title":"Chest X-ray images (Pneumonia)","author":"Mooney","key":"10.1016\/j.asoc.2022.109319_b112"},{"key":"10.1016\/j.asoc.2022.109319_b113","series-title":"2020 10th International Conference on Cloud Computing, Data Science Engineering","first-page":"227","article-title":"Feature extraction and classification of chest X-Ray images using CNN to detect pneumonia","author":"Sharma","year":"2020"},{"key":"10.1016\/j.asoc.2022.109319_b114","series-title":"2019 IEEE Canadian Conference of Electrical and Computer Engineering","first-page":"1","article-title":"Automatic detection of pneumonia on compressed sensing images using deep learning","author":"Islam","year":"2019"},{"key":"10.1016\/j.asoc.2022.109319_b115","series-title":"Proceedings of First International Conference on Computing, Communications, and Cyber-Security","first-page":"471","article-title":"Pneumonia detection using convolutional neural networks (CNNs)","author":"Kaushik","year":"2020"},{"issue":"03","key":"10.1016\/j.asoc.2022.109319_b116","doi-asserted-by":"crossref","DOI":"10.1142\/S0218001421510046","article-title":"Deep neural network-based screening model for COVID-19-infected patients using chest X-ray images","volume":"35","author":"Singh","year":"2021","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"year":"2020","series-title":"COVID-19 patients lungs X ray images 10000","author":"Sajid","key":"10.1016\/j.asoc.2022.109319_b117"},{"key":"10.1016\/j.asoc.2022.109319_b118","doi-asserted-by":"crossref","unstructured":"X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, R.M. Summers, ChestX-ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2017.","DOI":"10.1109\/CVPR.2017.369"},{"key":"10.1016\/j.asoc.2022.109319_b119","series-title":"2019 Scientific Meeting on Electrical-Electronics Biomedical Engineering and Computer Science","first-page":"1","article-title":"X-Ray chest image classification by a small-sized convolutional neural network","author":"Kesim","year":"2019"},{"key":"10.1016\/j.asoc.2022.109319_b120","doi-asserted-by":"crossref","unstructured":"J. Irvin, P. Rajpurkar, M. Ko, Y. Yu, S. Ciurea-Ilcus, C. Chute, H. Marklund, B. Haghgoo, R. Ball, K. Shpanskaya, et al., Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison, in: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33 no. 01, 2019, pp. 590\u2013597.","DOI":"10.1609\/aaai.v33i01.3301590"},{"year":"2020","series-title":"COVID-19 x rays","author":"Dadario","key":"10.1016\/j.asoc.2022.109319_b121"},{"year":"2020","series-title":"CoronaHack - chest X-Ray-dataset","author":"Govindaraj","key":"10.1016\/j.asoc.2022.109319_b122"},{"year":"2020","series-title":"Augmented COVID-19 X-ray images dataset","author":"Alqudah","key":"10.1016\/j.asoc.2022.109319_b123"},{"key":"10.1016\/j.asoc.2022.109319_b124","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103792","article-title":"Automated detection of COVID-19 cases using deep neural networks with X-ray images","volume":"121","author":"Ozturk","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2022.109319_b125","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.measurement.2019.05.076","article-title":"Identifying pneumonia in chest X-rays: A deep learning approach","volume":"145","author":"Jaiswal","year":"2019","journal-title":"Measurement"},{"key":"10.1016\/j.asoc.2022.109319_b126","series-title":"2020 IEEE International Conference on Electro Information Technology","first-page":"098","article-title":"Investigation of the performance of machine learning classifiers for pneumonia detection in chest X-ray images","author":"Al\u00a0Mamlook","year":"2020"},{"year":"2020","series-title":"Classification of pneumonia from X-ray images using siamese convolutional network 18 (3)","author":"Prayogo","key":"10.1016\/j.asoc.2022.109319_b127"},{"key":"10.1016\/j.asoc.2022.109319_b128","doi-asserted-by":"crossref","unstructured":"A. Saraiva, D. Santos, N. Carvalho\u00a0da Costa, J. Sousa, N. Ferreira, A. Valente, S. Soares, Models of Learning to Classify X-ray Images for the Detection of Pneumonia using Neural Networks, in: Bioimaging, 2019, pp. 76\u201383, http:\/\/dx.doi.org\/10.5220\/0007346600760083.","DOI":"10.5220\/0007346600760083"},{"issue":"2","key":"10.1016\/j.asoc.2022.109319_b129","first-page":"2475","article-title":"An efficient method for Covid-19 detection using light weight convolutional neural network","volume":"69","author":"Bekhet","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"10.1016\/j.asoc.2022.109319_b130","doi-asserted-by":"crossref","DOI":"10.1016\/j.sysarc.2019.101635","article-title":"A survey of techniques for optimizing deep learning on GPUs","volume":"99","author":"Mittal","year":"2019","journal-title":"J. Syst. Archit."},{"key":"10.1016\/j.asoc.2022.109319_b131","series-title":"Hybrid Artificial Intelligent Systems","first-page":"1","article-title":"Addressing the classification with imbalanced data: Open problems and new challenges on class distribution","author":"Fern\u00e1ndez","year":"2011"},{"key":"10.1016\/j.asoc.2022.109319_b132","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","first-page":"402","article-title":"Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation","author":"Li","year":"2019"},{"key":"10.1016\/j.asoc.2022.109319_b133","doi-asserted-by":"crossref","DOI":"10.1016\/j.ibmed.2021.100034","article-title":"Optimal hyperparameter selection of deep learning models for COVID-19 chest X-ray classification","volume":"5","author":"Adedigba","year":"2021","journal-title":"Intell.-Based Med."},{"key":"10.1016\/j.asoc.2022.109319_b134","doi-asserted-by":"crossref","unstructured":"C. Fernando, S. Kolonne, H. Kumarasinghe, D. Meedeniya, Chest Radiographs Classification Using Multi-model Deep Learning: A Comparative Study, in: Proceedings of the 2nd International Conference on Advanced Research in Computing, 2022, pp. 165\u2013170, http:\/\/dx.doi.org\/10.1109\/ICARC54489.2022.9753811.","DOI":"10.1109\/ICARC54489.2022.9753811"},{"issue":"7","key":"10.1016\/j.asoc.2022.109319_b135","first-page":"161","article-title":"U-Net based chest X-ray segmentation with ensemble classification for Covid-19 and pneumonia","volume":"18","author":"Kumarasinghe","year":"2022","journal-title":"International Journal of Online and Biomedical Engineering"},{"key":"10.1016\/j.asoc.2022.109319_b136","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/8890226","article-title":"Deep ensemble model for classification of novel coronavirus in chest X-ray images","volume":"2021","author":"Ahmad","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"10.1016\/j.asoc.2022.109319_b137","unstructured":"D. Tran, M.D. Hoffman, R.A. Saurous, E. Brevdo, K. Murphy, D.M. Blei, Deep probabilistic programming, in: Proceedings of the 5th International Conference on Learning Representations, ICLR, 2017, pp. 1\u201318, http:\/\/dx.doi.org\/10.48550\/arXiv.1701.03757."},{"key":"10.1016\/j.asoc.2022.109319_b138","doi-asserted-by":"crossref","first-page":"100340","DOI":"10.1016\/j.simpa.2022.100340","article-title":"Interpretable machine learning for brain tumour analysis using MRI and whole slide images","volume":"13","author":"Dasanayaka","year":"2022","journal-title":"Software Impacts"},{"issue":"3","key":"10.1016\/j.asoc.2022.109319_b139","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2020190043","article-title":"On the interpretability of artificial intelligence in radiology: challenges and opportunities","volume":"2","author":"Reyes","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"10.1016\/j.asoc.2022.109319_b140","first-page":"1","article-title":"A smart healthcare framework for detection and monitoring of COVID-19 using IoT and cloud computing","author":"Nasser","year":"2021","journal-title":"Neural Comput. Appl."}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494622004975?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494622004975?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,4,15]],"date-time":"2023-04-15T08:59:36Z","timestamp":1681549176000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494622004975"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9]]},"references-count":140,"alternative-id":["S1568494622004975"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2022.109319","relation":{},"ISSN":["1568-4946"],"issn-type":[{"type":"print","value":"1568-4946"}],"subject":[],"published":{"date-parts":[[2022,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Chest X-ray analysis empowered with deep learning: A systematic review","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2022.109319","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"109319"}}