{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T20:22:55Z","timestamp":1785874975153,"version":"3.56.0"},"reference-count":46,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,2,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The highly infectious coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, the seventh coronavirus. It is the longest pandemic in recorded history worldwide. Many countries are still reporting COVID-19 cases even in the fifth year of its emergence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The performance of various machine learning (ML) and deep learning (DL) models was studied for image-based classification of the lungs infected with COVID-19, pneumonia (viral and bacterial), and normal cases from the chest X-rays (CXRs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The\n                      <jats:italic>K<\/jats:italic>\n                      -nearest neighbour and logistics regression as the two ML models, and Visual Geometry Group-19, Vision transformer, and ConvMixer as the three DL models were included in the investigation to compare the brevity of the detection and classification of the cases.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among the investigated models, ConvMixer returned the best result in terms of accuracy, recall, precision,\n                      <jats:italic>F<\/jats:italic>\n                      1-score and area under the curve for both binary as well as multiclass classification. The pre-trained ConvMixer model outperformed the other four models in classifying. As per the performance observations, there was 97.1% accuracy for normal and COVID-19 + pneumonia-infected lungs, 98% accuracy for normal and COVID-19 infected lungs, 82% accuracy for normal + bacterial + viral infected lungs, and 98% accuracy for normal + pneumonia infected lungs. The DL models performed better than the ML models for binary and multiclass classification. The performance of these studied models was tried on other CXR image databases.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The suggested network effectively detected COVID-19 and different types of pneumonia by using CXR imagery. This could help medical sciences for timely and accurate diagnoses of the cases through bioimaging technology and the use of high-end bioinformatics tools.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/med-2024-1110","type":"journal-article","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T17:02:41Z","timestamp":1738688561000},"source":"Crossref","is-referenced-by-count":9,"title":["A comparative analysis of the binary and multiclass classified chest X-ray images of pneumonia and COVID-19 with ML and DL models"],"prefix":"10.1515","volume":"20","author":[{"given":"Madhumita","family":"Pal","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Government College of Engineering , Keonjhar , Odisha , India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ranjan K.","family":"Mohapatra","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Government College of Engineering , Keonjhar , 758 002, Odisha , India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashish K.","family":"Sarangi","sequence":"additional","affiliation":[{"name":"Department of Chemistry, School of Applied Sciences, Centurion University of Technology and Management , Balangir , Odisha , India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5638-1901","authenticated-orcid":false,"given":"Alok Ranjan","family":"Sahu","sequence":"additional","affiliation":[{"name":"Department of Botany, Vikash Degree College, Barahaguda, Canal Chowk , Bargarh , Odisha , India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3896-5831","authenticated-orcid":false,"given":"Snehasish","family":"Mishra","sequence":"additional","affiliation":[{"name":"School of Biotechnology, Campus-11, KIIT Deemed-to-be-University , Bhubaneswar , Odisha , India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alok","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Government College of Engineering , Keonjhar , Odisha , India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0086-8291","authenticated-orcid":false,"given":"Sushil Kumar","family":"Bhoi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Government College of Engineering Kalahandi , Kalahandi , Bhawanipatna , 766 003, Odisha , India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashraf Y.","family":"Elnaggar","sequence":"additional","affiliation":[{"name":"Department of Food Sciences and Nutrition, College of Science, Taif University , Taif , Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Islam H.","family":"El Azab","sequence":"additional","affiliation":[{"name":"Department of Food Sciences and Nutrition, College of Science, Taif University , Taif , Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Alissa","sequence":"additional","affiliation":[{"name":"Department of Medical Laboratory, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University , Al-Kharj , Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salah M.","family":"El-Bahy","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Turabah University College, Taif University , Taif , Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2025,2,4]]},"reference":[{"key":"2025122201304101740_j_med-2024-1110_ref_001","doi-asserted-by":"crossref","unstructured":"Mohapatra RK, Kuppili S, Suvvari TK, Kandi V, Behera A, Verma S, et al. 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