{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T18:56:51Z","timestamp":1783105011548,"version":"3.54.6"},"reference-count":199,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T00:00:00Z","timestamp":1734998400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Multimedia University, Malaysia","award":["MMUI\/230023"],"award-info":[{"award-number":["MMUI\/230023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>The global spread of SARS-CoV-2 has prompted a crucial need for accurate medical diagnosis, particularly in the respiratory system. Current diagnostic methods heavily rely on imaging techniques like CT scans and X-rays, but identifying SARS-CoV-2 in these images proves to be challenging and time-consuming. In this context, artificial intelligence (AI) models, specifically deep learning (DL) networks, emerge as a promising solution in medical image analysis. This article provides a meticulous and comprehensive review of imaging-based SARS-CoV-2 diagnosis using deep learning techniques up to May 2024. This article starts with an overview of imaging-based SARS-CoV-2 diagnosis, covering the basic steps of deep learning-based SARS-CoV-2 diagnosis, SARS-CoV-2 data sources, data pre-processing methods, the taxonomy of deep learning techniques, findings, research gaps and performance evaluation. We also focus on addressing current privacy issues, limitations, and challenges in the realm of SARS-CoV-2 diagnosis. According to the taxonomy, each deep learning model is discussed, encompassing its core functionality and a critical assessment of its suitability for imaging-based SARS-CoV-2 detection. A comparative analysis is included by summarizing all relevant studies to provide an overall visualization. Considering the challenges of identifying the best deep-learning model for imaging-based SARS-CoV-2 detection, the article conducts an experiment with twelve contemporary deep-learning techniques. The experimental result shows that the MobileNetV3 model outperforms other deep learning models with an accuracy of 98.11%. Finally, the article elaborates on the current challenges in deep learning-based SARS-CoV-2 diagnosis and explores potential future directions and methodological recommendations for research and advancement.<\/jats:p>","DOI":"10.7717\/peerj-cs.2517","type":"journal-article","created":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T08:09:49Z","timestamp":1735027789000},"page":"e2517","source":"Crossref","is-referenced-by-count":12,"title":["Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review"],"prefix":"10.7717","volume":"10","author":[{"given":"Md Shofiqul","family":"Islam","sequence":"first","affiliation":[{"name":"Computer Science and Engineering (CSE), Military Institute of Science and Technology (MIST), Dhaka, Bangladesh"},{"name":"Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Warun Ponds, Victoria, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2625-2348","authenticated-orcid":true,"given":"Fahmid","family":"Al Farid","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Multimedia University, Cyeberjaya, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9176-3537","authenticated-orcid":true,"given":"F. 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