{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T19:49:20Z","timestamp":1779133760163,"version":"3.51.4"},"reference-count":101,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100004054","name":"Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah","doi-asserted-by":"publisher","award":["GCV19-34-1441"],"award-info":[{"award-number":["GCV19-34-1441"]}],"id":[{"id":"10.13039\/501100004054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3054484","type":"journal-article","created":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T22:26:05Z","timestamp":1611613565000},"page":"20235-20254","source":"Crossref","is-referenced-by-count":90,"title":["Deep Learning Approaches for Detecting COVID-19 From Chest X-Ray Images: A Survey"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2059-7687","authenticated-orcid":false,"given":"Hanan S.","family":"Alghamdi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ghada","family":"Amoudi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Salma","family":"Elhag","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5295-4485","authenticated-orcid":false,"given":"Kawther","family":"Saeedi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jomanah","family":"Nasser","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"4278","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","author":"szegedy","year":"2017","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref33","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks Alex","volume":"1","author":"krizhevsky","year":"2012","journal-title":"Proc 25th Int Conf Neural Inf Process Syst"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2020.09.010"},{"key":"ref30","article-title":"Deep learning for screening COVID-19 using chest X-ray images","author":"basu","year":"2020","journal-title":"arXiv 2004 10507"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01829-7"},{"key":"ref35","author":"kumar","year":"2020","journal-title":"Detection of CoronavirusDisease (COVID-19) Based on Deep Features"},{"key":"ref34","article-title":"Improving Coronavirus (COVID-19) diagnosis using deep transfer learning","author":"razzak","year":"2020","journal-title":"medRxiv"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101794"},{"key":"ref27","article-title":"Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks","author":"narin","year":"2020","journal-title":"arXiv 2003 10849"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.cobme.2018.12.005"},{"key":"ref20","year":"2020","journal-title":"Figure\ufffd1—Covid-19 Clinical Library"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3010287"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s42600-020-00091-7"},{"key":"ref23","author":"mooney","year":"2018","journal-title":"Chest X-Ray Images (Pneumonia) |Kaggle"},{"key":"ref101","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref26","article-title":"Viral pneumonia screening on chest X-ray images using confidence-aware anomaly detection","author":"zhang","year":"2020","journal-title":"arXiv 2003 12338"},{"key":"ref100","first-page":"1050","author":"gal","year":"2016","journal-title":"Proc 33rd Int Conf Mach Learn (ICML)"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2535302"},{"key":"ref50","article-title":"ShuffleNet: An extremely efficient convolutional neural network for mobile devices","author":"zhang","year":"2017","journal-title":"arXiv 1707 01083"},{"key":"ref51","article-title":"Automated systems for detection of COVID-19 using chest X-ray images and lightweight convolutional neural networks","author":"alqudah","year":"2020","journal-title":"SquareTrade Research"},{"key":"ref59","article-title":"A deep convolutional neural network for COVID-19 detection using chest X-Rays","author":"bassi","year":"2020","journal-title":"arXiv 2005 01578"},{"key":"ref58","article-title":"Tracking and predicting COVID-19 radiological trajectory using deep learning on chest X-rays: Initial accuracy testing","volume":"1","author":"duchesne","year":"2020","journal-title":"medRxiv"},{"key":"ref57","article-title":"Accurate prediction of COVID-19 using chest X-ray images through deep feature learning model with smote and machine learning classifiers","author":"kumar","year":"2020","journal-title":"medRxiv"},{"key":"ref56","article-title":"Automated systems for detection of COVID-19 using chest X-ray images and lightweight convolutional neural networks","author":"alqudah","year":"2020","journal-title":"SquareTrade Research"},{"key":"ref55","first-page":"3857","article-title":"Dynamic routing between capsules","author":"sabour","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-21735-7_6"},{"key":"ref53","article-title":"CoroNet: A deep network architecture for semi-supervised task-based identification of COVID-19 from chest X-ray images","author":"khobahi","year":"2020","journal-title":"medRxiv"},{"key":"ref52","first-page":"37","article-title":"Autoencoders, unsupervised learning, and deep architectures","author":"baldi","year":"2012","journal-title":"Proc Unsupervised Transf Learn (ICML)"},{"key":"ref40","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc 3rd Int Conf Learn Represent Conf Track (ICLR)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.rmed.2020.105980"},{"key":"ref3","year":"2020","journal-title":"Advice on the Use of Point-of-Care Immunodiagnostic Tests for COVID-19"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020201160"},{"key":"ref5","article-title":"A Web-based diagnostic tool for COVID-19 using machine learning on chest radiographs (CXR)","author":"kana","year":"2020","journal-title":"medRxiv"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103792"},{"key":"ref7","article-title":"Automatic X-ray COVID-19 lung image classification system based on multi-level thresholding and support vector machine","author":"hassanien","year":"2020","journal-title":"medRxiv"},{"key":"ref49","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"arXiv 1704 04861"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"ref46","article-title":"COVIDX-net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images","author":"el-din hemdan","year":"2020","journal-title":"arXiv 2003 11055"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.3233\/XST-200715"},{"key":"ref41","article-title":"Deep convolutional neural networks to diagnose COVID-19 and other pneumonia diseases from posteroanterior chest X-Rays","author":"moutounet-cartan","year":"2020","journal-title":"arXiv 2005 00845"},{"key":"ref44","article-title":"Finding covid-19 from chest X-rays using deep learning on a small dataset","author":"hall","year":"2020","journal-title":"arXiv 2004 02060"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref73","article-title":"Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection","author":"ghoshal","year":"2020","journal-title":"arXiv 2003 10769"},{"key":"ref72","article-title":"Automatic detection of COVID-19 using X-ray images with deep convolutional neural networks and machine learning","author":"asif","year":"2020","journal-title":"medRxiv"},{"key":"ref71","year":"2020","journal-title":"Openi - an open access biomedical image search engine"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.02.010"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3033762"},{"key":"ref77","article-title":"Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: A first experience from Lombardy, Italy","author":"castiglioni","year":"2020","journal-title":"medRxiv"},{"key":"ref74","first-page":"1","article-title":"Training deep learning algorithms with weakly labeled pneumonia chest X-ray data for COVID-19 detection","author":"rajaraman","year":"2020","journal-title":"medRxiv"},{"key":"ref75","article-title":"A new modified deep convolutional neural network for detecting COVID-19 from X-ray images","author":"rahimzadeh","year":"2020","journal-title":"arXiv 2004 08052"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1016\/j.mehy.2020.109761"},{"key":"ref79","article-title":"Distinguishing l and h phenotypes of Covid-19 using a single X-ray image","author":"islam","year":"2020","journal-title":"medRxiv"},{"key":"ref60","article-title":"CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning","author":"rajpurkar","year":"2017","journal-title":"arXiv 1711 05225"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3010287"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-76550-z"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.174.1.1740071"},{"key":"ref64","year":"2018","journal-title":"RSNA Pneumonia Detection Challenge Dataset"},{"key":"ref65","year":"0","journal-title":"Italian Society of Medical and Interventional Radiology (SIRM) COVID?19 DATABASE"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"ref67","year":"2020","journal-title":"Twitter COVID?19 CXR Dataset ChestImaging (Twitter Account) Cardiothoracic Radiologist"},{"key":"ref68","year":"2020","journal-title":"Pediatric Pneumonia Chest X-ray"},{"key":"ref69","article-title":"Labeled optical coherence tomography (OCT) and chest X-Ray images for classification","volume":"2","author":"wang","year":"2018","journal-title":"Mendeley Data"},{"key":"ref2","year":"2020","journal-title":"WHO Warns That Few Have Developed Antibodies to Covid-19"},{"key":"ref1","year":"2020","journal-title":"Guidance and Standard Operating Procedure COVID-19 Virus Testing in NHS Laboratories"},{"key":"ref95","article-title":"FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis","author":"wong","year":"2018","journal-title":"arXiv 1809 05989"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01228-7"},{"key":"ref92","article-title":"Do explanations reflect decisions? A machine-centric strategy to quantify the performance of explainability algorithms","volume":"1910","author":"lin","year":"2019","journal-title":"arXiv 1910 07387"},{"key":"ref91","article-title":"Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: A first experience from Lombardy, Italy","author":"schiaffino","year":"2020","journal-title":"medRxiv"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105581"},{"key":"ref98","article-title":"Removing input features via a generative model to explain their attributions to an image classifier’s decisions","author":"agarwal","year":"2019","journal-title":"arXiv 1910 04256"},{"key":"ref99","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","author":"simonyan","year":"2013","journal-title":"arXiv 1312 6034"},{"key":"ref96","first-page":"1","article-title":"Striving for simplicity: The all convolutional net","author":"springenberg","year":"2015","journal-title":"Proc 3rd Int Conf Learn Represent Work Track (ICLR)"},{"key":"ref97","article-title":"InDepth: Layer-wise relevance propagation","author":"lindwurm","year":"2019","journal-title":"Medium"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2929365"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2766168"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2995800"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.26599\/BDMA.2018.9020001","article-title":"Applications of deep learning to MRI images: A survey","volume":"1","author":"liu","year":"2018","journal-title":"Big data mining and analytics"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2919951"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528162"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-018-1088-1"},{"key":"ref82","article-title":"COVID-DA: Deep domain adaptation from typical pneumonia to COVID-19","author":"zhang","year":"2020","journal-title":"arXiv 2005 01577"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s13244-018-0639-9"},{"key":"ref81","first-page":"1","article-title":"Automatic detection of COVID-19 infection from chest X-ray using deep learning","author":"medhi","year":"2020","journal-title":"medRxiv"},{"key":"ref84","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","volume":"9351","author":"ronneberger","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention—MICCAI"},{"key":"ref18","article-title":"COVID-19 image data collection: Prospective predictions are the future","author":"paul cohen","year":"2020","journal-title":"arXiv 2006 11988"},{"key":"ref83","article-title":"Robust screening of COVID-19 from chest X-ray via discriminative cost-sensitive learning","author":"li","year":"2020","journal-title":"arXiv 2004 12592"},{"key":"ref19","year":"2021","journal-title":"Radiopaedia org"},{"key":"ref80","article-title":"A cascade network for detecting COVID-19 using chest X-rays","author":"lv","year":"2020","journal-title":"arXiv 2005 01468"},{"key":"ref89","article-title":"CovidAID: COVID-19 detection using chest X-ray","author":"mangal","year":"2020","journal-title":"arXiv 2004 09803"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2993291"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2005.02.002"},{"key":"ref87","first-page":"475","article-title":"Two public chest X-ray datasets for computer-aided screening of pulmonary diseases","volume":"4","author":"jaeger","year":"2014","journal-title":"Quant Imaging Med Surg"},{"key":"ref88","article-title":"Corona hack: Chest X-ray-dataset","year":"0","journal-title":"Praveen"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09335581.pdf?arnumber=9335581","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:56:25Z","timestamp":1639770985000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9335581\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":101,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3054484","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}