{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T03:32:14Z","timestamp":1781667134400,"version":"3.54.5"},"reference-count":84,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T00:00:00Z","timestamp":1673222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"national research foundation (NRF)","award":["NRF2021R1A2C1014432"],"award-info":[{"award-number":["NRF2021R1A2C1014432"]}]},{"name":"national research foundation (NRF)","award":["NRF2022R1G1A1010226"],"award-info":[{"award-number":["NRF2022R1G1A1010226"]}]},{"DOI":"10.13039\/501100003725","name":"Ministry of Science and ICT (MSIT), South Korea through the Development Research","doi-asserted-by":"publisher","award":["NRF2021R1A2C1014432"],"award-info":[{"award-number":["NRF2021R1A2C1014432"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Ministry of Science and ICT (MSIT), South Korea through the Development Research","doi-asserted-by":"publisher","award":["NRF2022R1G1A1010226"],"award-info":[{"award-number":["NRF2022R1G1A1010226"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety, and it is anticipated that deep learning (DL) will be the most effective way of detecting COVID-19 and other chest diseases such as lung cancer (LC), tuberculosis (TB), pneumothorax (PneuTh), and pneumonia (Pneu). However, data sharing across hospitals is hampered by patients\u2019 right to privacy, leading to unexpected results from deep neural network (DNN) models. Federated learning (FL) is a game-changing concept since it allows clients to train models together without sharing their source data with anybody else. Few studies, however, focus on improving the model\u2019s accuracy and stability, whereas most existing FL-based COVID-19 detection techniques aim to maximize secondary objectives such as latency, energy usage, and privacy. In this work, we design a novel model named decision-making-based federated learning network (DMFL_Net) for medical diagnostic image analysis to distinguish COVID-19 from four distinct chest disorders including LC, TB, PneuTh, and Pneu. The DMFL_Net model that has been suggested gathers data from a variety of hospitals, constructs the model using the DenseNet-169, and produces accurate predictions from information that is kept secure and only released to authorized individuals. Extensive experiments were carried out with chest X-rays (CXR), and the performance of the proposed model was compared with two transfer learning (TL) models, i.e., VGG-19 and VGG-16 in terms of accuracy (ACC), precision (PRE), recall (REC), specificity (SPF), and F1-measure. Additionally, the DMFL_Net model is also compared with the default FL configurations. The proposed DMFL_Net + DenseNet-169 model achieves an accuracy of 98.45% and outperforms other approaches in classifying COVID-19 from four chest diseases and successfully protects the privacy of the data among diverse clients.<\/jats:p>","DOI":"10.3390\/s23020743","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T07:05:09Z","timestamp":1673247909000},"page":"743","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4402-5088","authenticated-orcid":false,"given":"Hassaan","family":"Malik","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9705-386X","authenticated-orcid":false,"given":"Ahmad","family":"Naeem","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7473-8441","authenticated-orcid":false,"given":"Rizwan Ali","family":"Naqvi","sequence":"additional","affiliation":[{"name":"Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woong-Kee","family":"Loh","sequence":"additional","affiliation":[{"name":"School of Computing, Gachon University, Seongnam 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kandati, D.R., and Gadekallu, T.R. 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