{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T04:15:10Z","timestamp":1780460110663,"version":"3.54.1"},"reference-count":158,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T00:00:00Z","timestamp":1664496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.<\/jats:p>","DOI":"10.3390\/jimaging8100267","type":"journal-article","created":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T23:39:31Z","timestamp":1665272371000},"page":"267","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Comprehensive Survey of Machine Learning Systems for COVID-19 Detection"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3458-5273","authenticated-orcid":false,"given":"Bayan","family":"Alsaaidah","sequence":"first","affiliation":[{"name":"Department of Computer Science, Prince Abdullah bin Ghazi Faculty of Information Technology and Communications, Al-Balqa Applied University, Salt 19117, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3296-1338","authenticated-orcid":false,"given":"Moh\u2019d Rasoul","family":"Al-Hadidi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Electrical Power Engineering and Computer Engineering, Faculty of Engineering, Al-Balqa Applied University, Salt 19117, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heba","family":"Al-Nsour","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Prince Abdullah bin Ghazi Faculty of Information Technology and Communications, Al-Balqa Applied University, Salt 19117, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4519-7110","authenticated-orcid":false,"given":"Raja","family":"Masadeh","sequence":"additional","affiliation":[{"name":"Computer Science Department, The World Islamic Sciences and Education University, Amman 11947, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nael","family":"AlZubi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Electrical Power Engineering and Computer Engineering, Faculty of Engineering, Al-Balqa Applied University, Salt 19117, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,30]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2021, August 20). Coronavirus Disease 2019 (COVID-19): Situation Report, 73. Available online: https:\/\/apps.who.int\/iris\/handle\/10665\/331686."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1177\/2347631120983481","article-title":"A literature review on impact of COVID-19 pandemic on teaching and learning","volume":"8","author":"Pokhrel","year":"2021","journal-title":"High. Educ. Future"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.jbusres.2020.05.030","article-title":"The impact of COVID-19 pandemic on corporate social responsibility and marketing philosophy","volume":"116","author":"He","year":"2020","journal-title":"J. Bus. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1788263","DOI":"10.1080\/16549716.2020.1788263","article-title":"Social determinants of health: The role of effective communication in the COVID-19 pandemic in developing countries","volume":"13","author":"Ataguba","year":"2020","journal-title":"Glob. Health Action"},{"key":"ref_5","first-page":"4738","article-title":"Glioblastomas brain tumour segmentation based on convolutional neural networks","volume":"10","author":"AlSaaidah","year":"2020","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_6","first-page":"2520","article-title":"Solving mammography problems of breast cancer detection using artificial neural networks and image processing techniques","volume":"5","author":"Alsaaidah","year":"2012","journal-title":"Indian J. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1148\/rg.2017160130","article-title":"Machine learn- ing for medical imaging","volume":"37","author":"Erickson","year":"2017","journal-title":"Radiographics"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.jmir.2019.09.005","article-title":"Machine learning and deep learning in medical imaging: Intelligent imaging","volume":"50","author":"Currie","year":"2019","journal-title":"J. Med. Imaging Radiat. Sci."},{"key":"ref_9","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1147\/rd.33.0210","article-title":"Some studies in machine learning using the game of checkers","volume":"3","author":"Samuel","year":"1959","journal-title":"IBM J. Res. Dev."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Oravec, M. (2014, January 10\u201312). Feature extraction and classification by machine learning methods for biometric recognition of face and iris. Proceedings of the ELMAR-2014, Zadar, Croatia.","DOI":"10.1109\/ELMAR.2014.6923301"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Amrane, M., Oukid, S., Gagaoua, I., and Ensari, T. (2018, January 18\u201319). Breast cancer classification using machine learning. Proceedings of the 2018 Electric Electronics, Computer Science, Biomedical Engineerings\u2019 Meeting (EBBT), Istanbul, Turkey.","DOI":"10.1109\/EBBT.2018.8391453"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dai, X., Spasi\u0107, I., Meyer, B., Chapman, S., and Andres, F. (2019, January 10\u201313). Machine learning on mobile: An on-device inference app for skin cancer detection. Proceedings of the 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC), Rome, Italy.","DOI":"10.1109\/FMEC.2019.8795362"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.12988\/ces.2015.59265","article-title":"Handwriting Recognition System Based on OCR","volume":"Volume 8","year":"2015","journal-title":"Contemporary Engineering Sciences"},{"key":"ref_15","first-page":"2151","article-title":"Voice Recognition System Using Wavelet Transform and Neural Networks","volume":"3","author":"Alsaaidah","year":"2011","journal-title":"J. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-031-02242-5","article-title":"Tensor voting: A perceptual organization approach to computer vision and machine learning","volume":"Volume 2","author":"Mordohai","year":"2006","journal-title":"Synthesis Lectures on Image, Video, and Multimedia Processing"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MCE.2016.2640698","article-title":"Deep Learning for Consumer Devices and Services: Pushing the limits for machine learning, artificial intelligence, and computer vision","volume":"6","author":"Lemley","year":"2017","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Al-Saaidah, B., Al-Nuaimy, W., Al-Taee, M., Al-Ataby, A., Young, I., and Al-Jubouri, Q. (September, January 31). Analysis of Embryonic Malformations in Zebrafish Larvae. Proceedings of the 2016 9th International Conference on Developments in e Systems Engineering (DeSE), Liverpool, UK.","DOI":"10.1109\/DeSE.2016.7"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Al-Saaidah, B., Al-Nuaimy, W., Al-Taee, M., Young, I., and Al-Jubouri, Q. (2017, January 17\u201318). Identification of tail curvature malformation in zebrafish embryos. Proceedings of the 2017 8th International Conference on Information Technology (ICIT), Amman, Jordan.","DOI":"10.1109\/ICITECH.2017.8080063"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Al-Saaidah, B., Al-Nuaimy, W., Al-Hadidi, M.R., and Young, I. (2018, January 3\u20135). Automatic counting system for zebrafish eggs using optical scanner. Proceedings of the 2018 9th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan.","DOI":"10.1109\/IACS.2018.8355450"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"347","DOI":"10.25046\/aj030435","article-title":"Zebrafish Larvae Classification based on Decision Tree Model: A Comparative Analysis","volume":"3","author":"AlSaaidah","year":"2018","journal-title":"Adv. Sci. Technol. Eng. Syst. J."},{"key":"ref_22","unstructured":"Xiao, X., Liu, B., Warnell, G., and Stone, P. (2020). Motion control for mobile robot navigation using machine learning: A survey. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Goil, A., Derry, M., and Argall, B.D. (2013, January 24\u201326). Using machine learning to blend human and robot controls for assisted wheelchair navigation. Proceedings of the 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR), Seattle, WA, USA.","DOI":"10.1109\/ICORR.2013.6650454"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.crad.2018.05.015","article-title":"Machine learning \u201cred dot\u201d: Open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification","volume":"73","author":"Yates","year":"2018","journal-title":"Clin. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1021\/acs.chas.0c00075","article-title":"Machine learning and deep learning in chemical health and safety: A systematic review of techniques and applications","volume":"27","author":"Jiao","year":"2020","journal-title":"ACS Chem. Health Saf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1561\/2000000039","article-title":"Deep learning: Methods and applications","volume":"7","author":"Deng","year":"2014","journal-title":"Found. Trends Signal Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1109\/TSMC.1971.4308320","article-title":"Polynomial theory of complex systems","volume":"SMC-1","author":"Ivakhnenko","year":"1971","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_30","unstructured":"Dechter, R. (1986, January 11\u201315). Learning while searching in constraint-satisfaction problems. Proceedings of the 5th National Conference on Artificial Intelligence, Philadelphia, PA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.knosys.2018.10.034","article-title":"Deep learning-based feature engineering for stock price movement prediction","volume":"164","author":"Long","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1145\/2347736.2347755","article-title":"A few useful things to know about machine learning","volume":"55","author":"Domingos","year":"2012","journal-title":"Commun. ACM"},{"key":"ref_34","unstructured":"(2021, July 12). \u201dPatient Page\u201d. ARRT\u2014The American Registry of Radiologic Technologists. Archived from the Original on 9 November 2014. Available online: https:\/\/www.arrt.org\/Patient-Public\/Patient-Page."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.jinf.2020.04.004","article-title":"CT imaging and clinical course of asymptomatic cases with COVID-19 pneumonia at admission in Wuhan, China","volume":"81","author":"Meng","year":"2020","journal-title":"J. Infect."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"100449","DOI":"10.1016\/j.imu.2020.100449","article-title":"Ensemble learning model for diagnosing COVID-19 from routine blood tests","volume":"21","author":"AlJame","year":"2020","journal-title":"Inform Med Unlocked."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"14250","DOI":"10.1038\/s41598-021-93719-2","article-title":"Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph","volume":"11","author":"Du","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Abayomi-Alli, O.O., Dama\u0161evi\u010dius, R., Maskeli\u016bnas, R., and Misra, S. (2022). An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples. Sensors, 22.","DOI":"10.3390\/s22062224"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1396","DOI":"10.1093\/clinchem\/hvaa200","article-title":"Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning","volume":"66","author":"Yang","year":"2020","journal-title":"Clin. Chem."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"110120","DOI":"10.1016\/j.chaos.2020.110120","article-title":"Comparison of deep learning approaches to predict COVID-19 infection","volume":"140","author":"Alakus","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4941","DOI":"10.1007\/s10462-021-10106-z","article-title":"Using artificial intelligence technology to fight COVID-19: A review","volume":"55","author":"Peng","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"116540","DOI":"10.1016\/j.eswa.2022.116540","article-title":"Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT","volume":"195","author":"Garg","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"208","DOI":"10.34306\/ajri.v3i2.659","article-title":"Analysis of Deep Learning Techniques for Chest X-ray Classification In Context Of COVID-19","volume":"3","author":"Agarwal","year":"2022","journal-title":"ADI J. Recent Innov."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e12759","DOI":"10.1111\/exsy.12759","article-title":"Review on COVID-19 diagnosis models based on machine learning and deep learning approaches","volume":"39","author":"Alyasseri","year":"2021","journal-title":"Expert Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5423","DOI":"10.1007\/s11042-020-09894-3","article-title":"A novel comparative study for detection of COVID-19 on CT lung images using texture analysis, machine learning, and deep learning methods","volume":"80","author":"Yasar","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1109\/TIP.2021.3058783","article-title":"Jcs: An explainable COVID-19 diagnosis system by joint classification and segmentation","volume":"30","author":"Wu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2376","DOI":"10.1109\/JBHI.2021.3074893","article-title":"COVID-19 Automatic Diagnosis with Radiographic Imaging: Explainable AttentionTransfer Deep Neural Networks","volume":"25","author":"Shi","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Goel, T., Murugan, R., Mirjalili, S., and Chakrabartty, D.K. (2021). Automatic screening of COVID-19 using an optimized generative adversarial network. Cogn. Comput., 1\u201316.","DOI":"10.1007\/s12559-020-09785-7"},{"key":"ref_49","unstructured":"Novelline, R. (1997). Squire\u2019s Fundamentals of Radiology, Harvard University Press. [5th ed.]."},{"key":"ref_50","unstructured":"Muller, R.A. (2010). Physics and Technology for Future Presidents: An Introduction to the Essential Physics Every World Leader Needs to Know, Princeton University Press."},{"key":"ref_51","unstructured":"US National Research Council (2006). Health Risks from Low Levels of Ionizing Radiation, BEIR 7 Phase 2."},{"key":"ref_52","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":"ref_53","doi-asserted-by":"crossref","unstructured":"Chowdhury, M.E., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M.A., Mah-bub, Z.B., Islam, K.R., Khan, M.S., Iqbal, A., and Al-Emadi, N. (2020). Can AI help in screening viral and COVID-19 pneumonia?. arXiv.","DOI":"10.1109\/ACCESS.2020.3010287"},{"key":"ref_54","unstructured":"Alom, M.Z., Rahman, M.M., Nasrin, M.S., Taha, T.M., and Asari, V.K. (2020). COVIDMTNet: COVID\u201319 Detection with Multitask Deep Learning Approaches. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Basu, S., and Mitra, S. (2020). Deep Learning for Screening COVID-19 using Chest X-ray Images. arXiv.","DOI":"10.1101\/2020.05.04.20090423"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"105608","DOI":"10.1016\/j.cmpb.2020.105608","article-title":"Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays","volume":"196","author":"Brunese","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Tabik, S., G\u00f3mez-R\u00edos, A., Martin-Rodriguez, J., Sevillano-Garcia, I., Rey-Area, M., Charte, D., Guirado, E., Suarez, J., Luengo, J., and Valero-Gonzalez, M. (2020). COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-ray images. arXiv.","DOI":"10.1109\/JBHI.2020.3037127"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Mobiny, A., Cicalese, P.A., Zare, S., Yuan, P., Abavisani, M., Wu, C.C., Ahuja, J., de Groot, P.M., and Van Nguyen, H. (2020). Radiologist-Level COVID-19 Detection Using CT scans with Detail-Oriented Capsule Networks. arXiv.","DOI":"10.1007\/978-3-030-59710-8_15"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K.N., and Mohammadi, A. (2020). Covid-caps: A capsule network-based framework for identification of COVID-19 cases from X-ray images. arXiv.","DOI":"10.3389\/frai.2021.598932"},{"key":"ref_60","unstructured":"Farooq, M., and Hafeez, A. (2020). Covid-resnet: A deep learning framework for screening of COVID-19 from radiographs. arXiv."},{"key":"ref_61","unstructured":"Hemdan, E.E., Shouman, M.A., and Karar, M.E. (2020). Covidx-net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images. arXiv."},{"key":"ref_62","unstructured":"(2021, September 04). IEEE8023\/Covid-Chestxray-Dataset. Available online: https:\/\/github.com\/ieee8023\/COVID-chestxray-dataset."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"26451","DOI":"10.1007\/s11042-021-10783-6","article-title":"Automatic prediction of COVID-19 from chest images using modified ResNet50","volume":"80","author":"Elpeltagy","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"427","DOI":"10.3389\/fmed.2020.00427","article-title":"Deep learning-based decision-tree classifier for COVID-19 diagnosis from chest X-ray imaging","volume":"7","author":"Yoo","year":"2020","journal-title":"Front. Med."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1007\/s10489-020-01826-w","article-title":"Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices","volume":"51","author":"Ahuja","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"147532","DOI":"10.1038\/s41598-020-74539-2","article-title":"Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: Combination of data augmentation methods","volume":"10","author":"Nishio","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"E88","DOI":"10.1148\/radiol.2020202944","article-title":"Diagnosis of COVID-19 Pneumonia Using Chest Radiography: Value of Artificial Intelligence","volume":"298","author":"Zhang","year":"2020","journal-title":"Radiology"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"91916","DOI":"10.1109\/ACCESS.2020.2994762","article-title":"Covidgan: Data augmentation using auxiliary classifier gan for improved COVID-19 detection","volume":"8","author":"Waheed","year":"2020","journal-title":"IEEE Access"},{"key":"ref_69","unstructured":"Engstrom, L., Tran, B., Tsipras, D., Schmidt, L., and Madry, A. (2019, January 6\u20139). A rotation and a translation suffice: Fooling CNNs with simple transformations. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.compbiomed.2012.12.004","article-title":"Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system","volume":"43","author":"Keshani","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1002\/mp.14676","article-title":"Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation","volume":"48","author":"Ma","year":"2021","journal-title":"Med. Phys."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","article-title":"Inf-net: Automatic COVID-19 lung infection segmentation from ct images","volume":"39","author":"Fan","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"108109","DOI":"10.1016\/j.patcog.2021.108109","article-title":"SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images","volume":"119","author":"Zhao","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_75","unstructured":"Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., Shen, D., and Shi, Y. (2020). Lung infection quantification of COVID-19 in CT images with deep learning. arXiv."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"5544742","DOI":"10.1155\/2021\/5544742","article-title":"Lung infection segmentation for COVID-19 pneumonia based on a cascade convolutional network from CT images","volume":"2021","author":"Ranjbarzadeh","year":"2021","journal-title":"BioMed. Res. Int."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Teixeira, L.O., Pereira, R.M., Bertolini, D., Oliveira, L.S., Nanni, L., Cavalcanti, G.D., and Costa, Y.M. (2021). Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images. Sensors, 21.","DOI":"10.3390\/s21217116"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"103869","DOI":"10.1016\/j.compbiomed.2020.103869","article-title":"CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization","volume":"122","author":"Mahmud","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"104066","DOI":"10.1016\/j.compbiomed.2020.104066","article-title":"COVID-19 detection in radiological text reports integrating entity recognition","volume":"127","author":"Luna","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"104348","DOI":"10.1016\/j.compbiomed.2021.104348","article-title":"Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases","volume":"132","author":"Ibrahim","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"104781","DOI":"10.1016\/j.compbiomed.2021.104781","article-title":"Automatic detection of COVID-19 using pruned GLCM-Based texture features and LDCRF classification","volume":"137","author":"Bakheet","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"110170","DOI":"10.1016\/j.chaos.2020.110170","article-title":"Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches","volume":"140","author":"Hassantabar","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"114054","DOI":"10.1016\/j.eswa.2020.114054","article-title":"Deep learning approaches for COVID-19 detection based on chest X-ray images","volume":"164","author":"Ismael","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1016\/j.aej.2021.06.024","article-title":"Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: Cough, voice, and breath","volume":"61","author":"Lella","year":"2022","journal-title":"Alex. Eng. J."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"104572","DOI":"10.1016\/j.compbiomed.2021.104572","article-title":"COVID-19 cough classification using machine learning and global smartphone recordings","volume":"135","author":"Pahar","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"100412","DOI":"10.1016\/j.imu.2020.100412","article-title":"A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images","volume":"20","author":"Islam","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"135","DOI":"10.36548\/jiip.2020.3.003","article-title":"Deep net model for detection of COVID-19 using radiographs based on roc analysis","volume":"2","author":"Dhaya","year":"2020","journal-title":"J. Innov. Image Process."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Sethy, P.K., and Behera, S.K. (2020). Detection of Coronavirus Disease (COVID-19) Based on Deep Features. Preprints, 2020030300.","DOI":"10.20944\/preprints202003.0300.v1"},{"key":"ref_89","unstructured":"Zhou, T., Canu, S., and Ruan, S. (2020). An automatic COVID-19 CT segmentation based on U-Net with attention mechanism. arXiv."},{"key":"ref_90","unstructured":"Tilborghs, S., Dirks, I., Fidon, L., Willems, S., Eelbode, T., Bertels, J., Ilsen, B., Brys, A., Dubbeldam, A., and Buls, N. (2020). Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients. arXiv."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s11042-021-11299-9","article-title":"Automatic deep learning system for COVID-19 infection quantification in chest CT","volume":"81","author":"Alirr","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Rehman, A., Naz, S., Khan, A., Zaib, A., and Razzak, I. (2020). Improving coronavirus (COVID-19) diagnosis using deep transfer learning. MedRxiv.","DOI":"10.1101\/2020.04.11.20054643"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"108190","DOI":"10.1016\/j.asoc.2021.108190","article-title":"Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images","volume":"115","author":"Novo","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Asif, S., Wenhui, Y., Jin, H., and Jinhai, S. (2020, January 11\u201314). December. Classification of COVID-19 from chest X-ray images using deep convolutional neural network. Proceedings of the 2020 IEEE 6th International Conference on Computer and Communications (ICCC), Chengdu, China.","DOI":"10.1109\/ICCC51575.2020.9344870"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Medhi, K., Jamil, M., and Hussain, M.I. (2020). Automatic detection of COVID-19 infection from chest X-ray using deep learning. Medrxiv.","DOI":"10.1101\/2020.05.10.20097063"},{"key":"ref_96","unstructured":"Jim, A.A.J., Rafi, I., Chowdhury, M.S., Sikder, N., Mahmud, M.P., Rubaiee, S., Masud, M., Bairagi, A.K., Bhakta, K., and Nahid, A.A. (2020). An automatic computer-based method for fast and accurate COVID-19 diagnosis. medRxiv."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Hasan, M.K., Alam, M.A., Dahal, L., Elahi, M.T.E., Roy, S., Wahid, S.R., Mart\u00ed, R., and Khanal, B. (2020). Challenges of deep learning methods for COVID-19 detection using public datasets. medRxiv.","DOI":"10.1101\/2020.11.07.20227504"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Asif, S., and Amjad, K. (2020). Automatic COVID-19 Detection from chest radiographic images using Convolutional Neural Network. medRxiv.","DOI":"10.1101\/2020.11.08.20228080"},{"key":"ref_99","unstructured":"Zhang, W., Pogorelsky, B., Loveland, M., and Wolf, T. (2021). Classification of COVID-19 X-ray images using a combination of deep and handcrafted features. arXiv."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"102008","DOI":"10.1016\/j.compmedimag.2021.102008","article-title":"DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays","volume":"94","author":"Mamalakis","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_101","unstructured":"Ghassemi, N., Shoeibi, A., Khodatars, M., Heras, J., Rahimi, A., Zare, A., Pachori, R.B., and Gorriz, J.M. (2021). Automatic diagnosis of COVID-19 from ct images using cyclegan and transfer learning. arXiv."},{"key":"ref_102","first-page":"490","article-title":"An Automatic Classification of COVID with J48 and Simple K-Means using Weka","volume":"13","author":"Chakkaravarthy","year":"2020","journal-title":"Int. J. Future Gener. Commun. Netw."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1007\/s40846-020-00529-4","article-title":"Extracting possibly representative COVID-19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases","volume":"40","author":"Apostolopoulos","year":"2020","journal-title":"J. Med. Biol. Eng."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Mahdy, L.N., Ezzat, K.A., Elmousalami, H.H., Ella, H.A., and Hassanien, A.E. (2020). Automatic X-ray COVID-19 lung image classification system based on multi-level thresholding and support vector machine. MedRxiv.","DOI":"10.1101\/2020.03.30.20047787"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"5682","DOI":"10.1080\/07391102.2020.1788642","article-title":"Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning","volume":"39","author":"Jaiswal","year":"2021","journal-title":"J. Biomol. Struct. Dyn."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"5836","DOI":"10.1080\/07391102.2021.1875049","article-title":"Adopt: Automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images","volume":"40","author":"Dhiman","year":"2021","journal-title":"J. Biomol. Struct. Dyn."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"6480","DOI":"10.1109\/TII.2021.3057524","article-title":"COVID-19: Automatic detection of the novel coronavirus disease from ct images using an optimized convolutional neural network","volume":"17","author":"Castiglione","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.radi.2020.10.018","article-title":"Automatic detection of COVID-19 from chest radiographs using deep learning","volume":"27","author":"Pandit","year":"2021","journal-title":"Radiography"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"100505","DOI":"10.1016\/j.imu.2020.100505","article-title":"EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers","volume":"22","author":"Saha","year":"2021","journal-title":"Inform. Med. Unlocked"},{"key":"ref_110","first-page":"16","article-title":"The role of artificial intelligence in management of critical COVID-19 patients","volume":"5","author":"Rahmatizadeh","year":"2020","journal-title":"J. Cell. Mol. Anesth."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2021.04.008","article-title":"A critic evaluation of methods for COVID-19 automatic detection from X-ray images","volume":"76","author":"Maguolo","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s42600-020-00091-7","article-title":"IKONOS: An intelligent tool to support diagnosis of COVID-19 by texture analysis of X-ray images","volume":"38","author":"Gomes","year":"2020","journal-title":"Res. Biomed. Eng."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"2864","DOI":"10.1007\/s10489-020-02010-w","article-title":"An automatic approach based on CNN architecture to detect Covid-19 disease from chest X-ray images","volume":"51","author":"Hira","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Barua, P.D., Muhammad Gowdh, N.F., Rahmat, K., Ramli, N., Ng, W.L., Chan, W.Y., Kuluozturk, M., Dogan, S., Baygin, M., and Yaman, O. (2021). Automatic COVID-19 detection using exemplar hybrid deep features with X-ray images. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18158052"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1002\/ima.22525","article-title":"An integrated feature frame work for automated segmentation of COVID-19 infection from lung CT images","volume":"31","author":"Selvaraj","year":"2021","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1002\/ima.22527","article-title":"Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism","volume":"31","author":"Zhou","year":"2021","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_117","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":"ref_118","doi-asserted-by":"crossref","first-page":"2504","DOI":"10.1002\/jemt.23713","article-title":"Deep convolutional neural networks for COVID-19 automatic diagnosis","volume":"84","author":"Emara","year":"2021","journal-title":"Microsc. Res. Technol."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Albadr, M.A.A., Tiun, S., Ayob, M., Al-Dhief, F.T., Omar, K., and Hamzah, F.A. (2020). Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0242899"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Lorencin, I., Baressi \u0160egota, S., An\u0111eli\u0107, N., Blagojevi\u0107, A., \u0160u\u0161ter\u0161i\u0107, T., Proti\u0107, A., Arsenijevi\u0107, M., \u0106abov, T., Filipovi\u0107, N., and Car, Z. (2021). Automatic evaluation of the lung condition of COVID-19 patients using X-ray images and convolutional neural networks. J. Pers. Med., 11.","DOI":"10.3390\/jpm11010028"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s40747-020-00199-4","article-title":"Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans","volume":"7","author":"Karar","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1007\/s11760-020-01820-2","article-title":"Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images","volume":"15","author":"Kc","year":"2021","journal-title":"Signal Image Video Process."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"107848","DOI":"10.1016\/j.patcog.2021.107848","article-title":"Multi-task contrastive learning for automatic CT and X-ray diagnosis of COVID-19","volume":"114","author":"Li","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"104037","DOI":"10.1016\/j.compbiomed.2020.104037","article-title":"Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation","volume":"126","author":"Amyar","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1007\/s10044-021-00984-y","article-title":"Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks","volume":"24","author":"Narin","year":"2021","journal-title":"Pattern Anal. Appl."},{"key":"ref_126","first-page":"S117","article-title":"Deep transfer learning with apache spark to detect COVID-19 in chest X-ray images","volume":"23","author":"Benbrahim","year":"2020","journal-title":"Rom. J. Inf. Sci. Technol."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1152\/physiolgenomics.00084.2020","article-title":"Implementation of convolutional neural network approach for COVID-19 disease detection","volume":"52","author":"Irmak","year":"2020","journal-title":"Physiol. Genom."},{"key":"ref_128","first-page":"1141","article-title":"A novel approach of CT images feature analysis and prediction to screen for corona virus disease (COVID-19)","volume":"11","author":"Farid","year":"2020","journal-title":"Int. J. Sci. Eng. Res."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Sarker, S., Tan, L., Wen Jie, M., Shan Shan, R., Ali, M.A., Bilal, M., Qiu, Z., Kumar Mondal, S., and Tiwari, P. (2020). Automatic Classification Approach for Detecting COVID-19 using Deep Convolutional Neural Networks. Preprints, 2020090524.","DOI":"10.20944\/preprints202009.0524.v1"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"E65","DOI":"10.1148\/radiol.2020200905","article-title":"Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT","volume":"296","author":"Li","year":"2020","journal-title":"Radiology"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s12539-020-00403-6","article-title":"A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images","volume":"13","author":"Rasheed","year":"2021","journal-title":"Interdiscip. Sci. Comput. Life Sci."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1007\/s00530-021-00826-1","article-title":"Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images","volume":"28","author":"Ravi","year":"2021","journal-title":"Multimed. Syst."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/s12938-020-00807-x","article-title":"Rapid identification of COVID-19 severity in CT scans through classification of deep features","volume":"19","author":"Yu","year":"2020","journal-title":"BioMed. Eng. Online"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1186\/s12938-020-00831-x","article-title":"Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection","volume":"19","author":"Hussain","year":"2020","journal-title":"BioMed. Eng. Online"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"18926","DOI":"10.1038\/s41598-020-76141-y","article-title":"The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia","volume":"10","author":"Tan","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1007\/s40747-020-00216-6","article-title":"A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images","volume":"7","author":"Shankar","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1007\/s10096-020-03901-z","article-title":"Classification of COVID-19 patients from chest CT images using multi-objective differential evolution\u2013based convolutional neural networks","volume":"39","author":"Singh","year":"2020","journal-title":"Eur. J. Clin. Microbiol. Infect. Dis."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"3044","DOI":"10.1007\/s10489-020-02149-6","article-title":"Densely connected convolutional networks-based COVID-19 screening model","volume":"51","author":"Singh","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"2516","DOI":"10.1007\/s00259-020-04929-1","article-title":"End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT","volume":"47","author":"Song","year":"2020","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Tan, L., Yu, K., Bashir, A.K., Cheng, X., Ming, F., Zhao, L., and Zhou, X. (2021). Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: A deep learning approach. Neural Comput. Appl., 1\u201314.","DOI":"10.1007\/s00521-021-06219-9"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Loey, M., Smarandache, F., and MKhalifa, N.E. (2020). Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning. Symmetry, 12.","DOI":"10.3390\/sym12040651"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/JAS.2020.1003393","article-title":"Automatic detection of COVID-19 infection using chest X-ray images through transfer learning","volume":"8","author":"Ohata","year":"2020","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"107747","DOI":"10.1016\/j.patcog.2020.107747","article-title":"Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images","volume":"114","author":"Oulefki","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_144","unstructured":"Haque, K.F., Haque, F.F., Gandy, L., and Abdelgawad, A. (2020, January 17\u201318). Automatic detection of COVID-19 from chest X-ray images with convolutional neural networks. Proceedings of the 2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE), Southend, UK."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"28716","DOI":"10.1109\/ACCESS.2021.3058854","article-title":"Auto-diagnosis of COVID-19 using lung CT images with semi-supervised shallow learning network","volume":"9","author":"Konar","year":"2021","journal-title":"IEEE Access"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.bbe.2021.01.002","article-title":"A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images","volume":"41","author":"Joshi","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"662","DOI":"10.18517\/ijaseit.10.2.11446","article-title":"Performance evaluation of the NASNet convolutional network in the automatic identification of COVID-19","volume":"10","author":"Jacinto","year":"2020","journal-title":"Int. J. Adv. Sci. Eng. Inf. Technol."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1109\/TMI.2020.2995965","article-title":"A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT","volume":"39","author":"Wang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_149","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":"ref_150","doi-asserted-by":"crossref","first-page":"256","DOI":"10.4103\/crst.crst_28_21","article-title":"Novel artificial intelligence algorithm for automatic detection of COVID-19 abnormalities in computed tomography images","volume":"4","author":"Bharadwaj","year":"2021","journal-title":"Cancer Res. Stat. Treat."},{"key":"ref_151","first-page":"49","article-title":"CCBlock: An effective use of deep learning for automatic diagnosis of COVID-19 using X-ray images","volume":"38","author":"Jeryo","year":"2020","journal-title":"Res. Biomed. Eng."},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Pustokhin, D.A., Pustokhina, I.V., Dinh, P.N., Phan, S.V., Nguyen, G.N., and Joshi, G.P. (2020). An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19. J. Appl. Stat.","DOI":"10.1080\/02664763.2020.1849057"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"103792","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":"ref_154","doi-asserted-by":"crossref","unstructured":"Zaffino, P., Marzullo, A., Moccia, S., Calimeri, F., De Momi, E., Bertucci, B., Arcuri, P.P., and Spadea, M.F. (2021). An open-source COVID-19 ct dataset with automatic lung tissue classification for radiomics. Bioengineering, 8.","DOI":"10.3390\/bioengineering8020026"},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"108966","DOI":"10.1016\/j.asoc.2022.108966","article-title":"Explainable artificial intelligence-based edge fuzzy images for COVID-19 detection and identification","volume":"123","author":"Hu","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"7377502","DOI":"10.1155\/2022\/7377502","article-title":"Ensemble deep learning and internet of things-based automated COVID-19 diagnosis framework","volume":"10","author":"Kini","year":"2022","journal-title":"Contrast Media Mol. Imaging"},{"key":"ref_157","first-page":"662343","article-title":"Explainable Machine Learning for COVID-19 Pneumonia Classification with Texture-Based Features Extraction in Chest Radiography","volume":"3","author":"Mattjie","year":"2021","journal-title":"Front. Digit. Health"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1049\/ipr2.12474","article-title":"A COVID-19 CXR image recognition method based on MSA-DDCovidNet","volume":"16","author":"Wang","year":"2022","journal-title":"IET Image Process."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/10\/267\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:44:39Z","timestamp":1760143479000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/10\/267"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,30]]},"references-count":158,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["jimaging8100267"],"URL":"https:\/\/doi.org\/10.3390\/jimaging8100267","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,30]]}}}