{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T05:46:59Z","timestamp":1789624019071,"version":"build-2803163510"},"reference-count":48,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T00:00:00Z","timestamp":1722902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation","award":["IUSE 2216396"],"award-info":[{"award-number":["IUSE 2216396"]}]},{"name":"National Science Foundation","award":["ECR-PEER-1935454"],"award-info":[{"award-number":["ECR-PEER-1935454"]}]},{"name":"National Science Foundation","award":["FIPSE P116S210004"],"award-info":[{"award-number":["FIPSE P116S210004"]}]},{"name":"National Science Foundation","award":["MSEIP P120A220044"],"award-info":[{"award-number":["MSEIP P120A220044"]}]},{"name":"U.S. Department of Education","award":["IUSE 2216396"],"award-info":[{"award-number":["IUSE 2216396"]}]},{"name":"U.S. Department of Education","award":["ECR-PEER-1935454"],"award-info":[{"award-number":["ECR-PEER-1935454"]}]},{"name":"U.S. Department of Education","award":["FIPSE P116S210004"],"award-info":[{"award-number":["FIPSE P116S210004"]}]},{"name":"U.S. Department of Education","award":["MSEIP P120A220044"],"award-info":[{"award-number":["MSEIP P120A220044"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network\u2019s determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first propose a VGG-16-architecture-based deep learning approach in combination with image enhancement, segmentation-based region of interest (ROI) cropping, and data augmentation steps to enhance classification accuracy. Later, a multi-layer Gradient CAM (ML-Grad-CAM) algorithm is integrated to generate a class-specific saliency map for improved visualization in CXR images. We also define and calculate a Severity Assessment Index (SAI) from the saliency map to quantitatively measure infection severity. The trained model achieved an accuracy score of 96.44% for the three-class CXR classification task, i.e., COVID-19, pneumonia, and normal (healthy patients), outperforming many existing techniques in the literature. The saliency maps generated from the proposed ML-GRAD-CAM algorithm are compared with the original Gran-CAM algorithm.<\/jats:p>","DOI":"10.3390\/diagnostics14161699","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T08:07:59Z","timestamp":1722931679000},"page":"1699","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Machine-Learning-Enabled Diagnostics with Improved Visualization of Disease Lesions in Chest X-ray Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0437-2587","authenticated-orcid":false,"given":"Md Fashiar","family":"Rahman","sequence":"first","affiliation":[{"name":"Department of Industrial, Manufacturing and Systems Engineering, The University of Texas, El Paso, TX 79968, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3903-529X","authenticated-orcid":false,"given":"Tzu-Liang (Bill)","family":"Tseng","sequence":"additional","affiliation":[{"name":"Department of Industrial, Manufacturing and Systems Engineering, The University of Texas, El Paso, TX 79968, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2122-2572","authenticated-orcid":false,"given":"Michael","family":"Pokojovy","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Old Dominion University, Norfolk, VA 23529, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"McCaffrey","sequence":"additional","affiliation":[{"name":"Department of Radiology, The University of Texas Medical Branch, Galveston, TX 77550, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Walser","sequence":"additional","affiliation":[{"name":"Department of Radiology, The University of Texas Medical Branch, Galveston, TX 77550, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4707-0455","authenticated-orcid":false,"given":"Scott","family":"Moen","sequence":"additional","affiliation":[{"name":"Department of Radiology, The University of Texas Medical Branch, Galveston, TX 77550, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Vo","sequence":"additional","affiliation":[{"name":"Department of Radiology, The University of Texas Medical Branch, Galveston, TX 77550, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8790-7769","authenticated-orcid":false,"given":"Johnny C.","family":"Ho","sequence":"additional","affiliation":[{"name":"Department of Management and Marketing, Turner College of Business, Columbus State University, Columbus, GA 31907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1111\/tmi.13383","article-title":"The COVID-19 epidemic","volume":"25","author":"Velavan","year":"2020","journal-title":"Trop. 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