{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T15:36:31Z","timestamp":1787585791937,"version":"build-2736575974"},"reference-count":44,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T00:00:00Z","timestamp":1674345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Since December 2019, the coronavirus disease has significantly affected millions of people. Given the effect this disease has on the pulmonary systems of humans, there is a need for chest radiographic imaging (CXR) for monitoring the disease and preventing further deaths. Several studies have been shown that Deep Learning models can achieve promising results for COVID-19 diagnosis towards the CXR perspective. In this study, five deep learning models were analyzed and evaluated with the aim of identifying COVID-19 from chest X-ray images. The scope of this study is to highlight the significance and potential of individual deep learning models in COVID-19 CXR images. More specifically, we utilized the ResNet50, ResNet101, DenseNet121, DenseNet169 and InceptionV3 using Transfer Learning. All models were trained and validated on the largest publicly available repository for COVID-19 CXR images. Furthermore, they were evaluated on unknown data that was not used for training or validation, authenticating their performance and clarifying their usage in a medical scenario. All models achieved satisfactory performance where ResNet101 was the superior model achieving 96% in Precision, Recall and Accuracy, respectively. Our outcomes show the potential of deep learning models on COVID-19 medical offering a promising way for the deeper understanding of COVID-19.<\/jats:p>","DOI":"10.3390\/ijerph20032035","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T02:27:45Z","timestamp":1674440865000},"page":"2035","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["COVID-19 Classification on Chest X-ray Images Using Deep Learning Methods"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3113-5341","authenticated-orcid":false,"given":"Marios","family":"Constantinou","sequence":"first","affiliation":[{"name":"Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49132 Corfu, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Themis","family":"Exarchos","sequence":"additional","affiliation":[{"name":"Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49132 Corfu, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aristidis G.","family":"Vrahatis","sequence":"additional","affiliation":[{"name":"Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49132 Corfu, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0053-7847","authenticated-orcid":false,"given":"Panagiotis","family":"Vlamos","sequence":"additional","affiliation":[{"name":"Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49132 Corfu, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,22]]},"reference":[{"key":"ref_1","unstructured":"(2022, June 17). 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