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Rashid, “Systematic Review of AI Techniques in the Detection & Classification of COVID-19”, Elsevier, 2020" }, { "key": "ref14", "unstructured": "Shuo Wang and Yao Lu, “The role of imaging in the detection and management of COVID-19: a review”, IEEE 2020. Published by: Longman Publishers www.jst.org.in 351 | Page" } ], "record": { "registrant": "Longman Publishers", "registered": "2026-09-11", "updated": "2026-09-20", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "url_after": "https://www.jst.org.in/index.php/pub/article/view/714", "sha256_before": "41244b0213f7cdab260f18b39975d358d3c323947aadfad17cfb9a44aec5799c", "sha256_after": "6cc3b6ba45aefeed7b451edab6ca25c0c3e64eb0bd566a4ab5e590ee7619c871" } ] }