{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T00:48:11Z","timestamp":1771030091473,"version":"3.50.1"},"reference-count":45,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2021,6,11]],"date-time":"2021-06-11T00:00:00Z","timestamp":1623369600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Imaging Syst Tech"],"published-print":{"date-parts":[[2021,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Considering the prevailing scenario of COVID\u201019 pandemic, early detection of the disease is an important and crucial step in disease management. Early detection and correct treatment may limit disease progression to severe levels and prevent deaths. In addition, early isolation of infected patients will lead to control transmission rate and will possibly reduce the stress on the present healthcare system. Currently, the most common and reliable testing method available for COVID\u201019 diagnosis is real\u2010time reverse transcription\u2010polymerase chain reaction (rRT\u2010PCR) test. However, the chest radiological (X\u2010ray) imaging can be used as an alternate method to rRT\u2010PCR test, and early COVID\u201019 symptoms can be investigated by critical examination of patient's chest scans. In the present work, a novel machine learning (ML)\u2010based analytical framework is developed for automatic detection of COVID\u201019 using chest X\u2010ray (CXR) images of plausible patients. The framework is designed, trained, and validated to identify four classes of CXR images namely, healthy, bacterial pneumonia, viral pneumonia, and COVID\u201019. The experimental results pose the proposed framework as a potential candidate for COVID\u201019 disease diagnosis using CXR images, with training, validation, and testing accuracy of 92.4%, 88.24%, and 87.13%, respectively, in four\u2010class classification. The comparative analysis demonstrates the better capabilities of the proposed framework COVID\u201019 detection along with other types of pneumonia.<\/jats:p>","DOI":"10.1002\/ima.22613","type":"journal-article","created":{"date-parts":[[2021,6,11]],"date-time":"2021-06-11T09:09:49Z","timestamp":1623402589000},"page":"1105-1119","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A novel machine learning\u2010based analytical framework for automatic detection of <scp>COVID<\/scp>\u201019 using chest <scp>X\u2010ray<\/scp> images"],"prefix":"10.1002","volume":"31","author":[{"given":"Shikhar","family":"Johri","sequence":"first","affiliation":[{"name":"Analytics and Insight Unit Tata Consultancy Services Pvt. Ltd.  Bangalore India"}]},{"given":"Mehendi","family":"Goyal","sequence":"additional","affiliation":[{"name":"RnD Division DOCONVID AI Bestech Business Tower  Mohali India"},{"name":"Department of Biotechnology Thapar Institute of Engineering &amp; Technology  Patiala India"}]},{"given":"Sahil","family":"Jain","sequence":"additional","affiliation":[{"name":"Department of Biotechnology Thapar Institute of Engineering &amp; Technology  Patiala India"},{"name":"University Institute of Biotechnology Chandigarh University  Mohali India"}]},{"given":"Manoj","family":"Baranwal","sequence":"additional","affiliation":[{"name":"Department of Biotechnology Thapar Institute of Engineering &amp; Technology  Patiala India"}]},{"given":"Vinay","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering Thapar Institute of Engineering &amp; Technology  Patiala India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0476-4529","authenticated-orcid":false,"given":"Rahul","family":"Upadhyay","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering Thapar Institute of Engineering &amp; Technology  Patiala India"}]}],"member":"311","published-online":{"date-parts":[[2021,6,11]]},"reference":[{"key":"e_1_2_12_2_1","unstructured":"WHO.WHO statement regarding cluster of pneumonia cases in Wuhan. 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