{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:38:43Z","timestamp":1787027923573,"version":"build-2736575974"},"reference-count":88,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:00:00Z","timestamp":1675296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jagannath University Research","award":["JnU\/Research\/gapro\/2021\u20132022\/Science\/80"],"award-info":[{"award-number":["JnU\/Research\/gapro\/2021\u20132022\/Science\/80"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>COVID-19 is a severe respiratory contagious disease that has now spread all over the world. COVID-19 has terribly impacted public health, daily lives and the global economy. Although some developed countries have advanced well in detecting and bearing this coronavirus, most developing countries are having difficulty in detecting COVID-19 cases for the mass population. In many countries, there is a scarcity of COVID-19 testing kits and other resources due to the increasing rate of COVID-19 infections. Therefore, this deficit of testing resources and the increasing figure of daily cases encouraged us to improve a deep learning model to aid clinicians, radiologists and provide timely assistance to patients. In this article, an efficient deep learning-based model to detect COVID-19 cases that utilizes a chest X-ray images dataset has been proposed and investigated. The proposed model is developed based on ResNet50V2 architecture. The base architecture of ResNet50V2 is concatenated with six extra layers to make the model more robust and efficient. Finally, a Grad-CAM-based discriminative localization is used to readily interpret the detection of radiological images. Two datasets were gathered from different sources that are publicly available with class labels: normal, confirmed COVID-19, bacterial pneumonia and viral pneumonia cases. Our proposed model obtained a comprehensive accuracy of 99.51% for four-class cases (COVID-19\/normal\/bacterial pneumonia\/viral pneumonia) on Dataset-2, 96.52% for the cases with three classes (normal\/ COVID-19\/bacterial pneumonia) and 99.13% for the cases with two classes (COVID-19\/normal) on Dataset-1. The accuracy level of the proposed model might motivate radiologists to rapidly detect and diagnose COVID-19 cases.<\/jats:p>","DOI":"10.3390\/diagnostics13030551","type":"journal-article","created":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T03:04:23Z","timestamp":1675393463000},"page":"551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2379-2253","authenticated-orcid":false,"given":"Md. Khabir Uddin","family":"Ahamed","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3075-9048","authenticated-orcid":false,"given":"Md Manowarul","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Ashraf","family":"Uddin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh"},{"name":"School of Information Technology, Geelong, Deakin University, Geelong, VIC 3216, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnisha","family":"Akhter","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Uzzal Kumar","family":"Acharjee","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4414-2751","authenticated-orcid":false,"given":"Bikash Kumar","family":"Paul","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh"},{"name":"Department of Software Engineering, Daffodil International University, Dhaka 1207, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0756-1006","authenticated-orcid":false,"given":"Mohammad Ali","family":"Moni","sequence":"additional","affiliation":[{"name":"Artificial Intelligence & Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"E7","DOI":"10.1038\/s41586-020-2202-3","article-title":"Author Correction: A new coronavirus associated with human respiratory disease in China","volume":"580","author":"Wu","year":"2020","journal-title":"Nature"},{"key":"ref_2","unstructured":"(2021, March 20). 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