{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T09:46:09Z","timestamp":1775209569550,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Regional Government of Andalusia (Spain)","award":["PCM-00006"],"award-info":[{"award-number":["PCM-00006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>On 11 February 2020, the prevalent outbreak of COVID-19, a coronavirus illness, was declared a global pandemic. Since then, nearly seven million people have died and over 765 million confirmed cases of COVID-19 have been reported. The goal of this study is to develop a diagnostic tool for detecting COVID-19 infections more efficiently. Currently, the most widely used method is Reverse Transcription Polymerase Chain Reaction (RT-PCR), a clinical technique for infection identification. However, RT-PCR is expensive, has limited sensitivity, and requires specialized medical expertise. One of the major challenges in the rapid diagnosis of COVID-19 is the need for reliable imaging, particularly X-ray imaging. This work takes advantage of artificial intelligence (AI) techniques to enhance diagnostic accuracy by automating the detection of COVID-19 infections from chest X-ray (CXR) images. We obtained and analyzed CXR images from the Kaggle public database (4035 images in total), including cases of COVID-19, viral pneumonia, pulmonary opacity, and healthy controls. By integrating advanced techniques with transfer learning from pre-trained convolutional neural networks (CNNs), specifically InceptionV3, ResNet50, and Xception, we achieved an accuracy of 95%, significantly higher than the 85.5% achieved with ResNet50 alone. Additionally, our proposed method, CXR-DNNs, can accurately distinguish between three different types of chest X-ray images for the first time. This computer-assisted diagnostic tool has the potential to significantly enhance the speed and accuracy of COVID-19 diagnoses.<\/jats:p>","DOI":"10.3390\/jimaging10120328","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T03:59:43Z","timestamp":1734580783000},"page":"328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["X-Ray Image-Based Real-Time COVID-19 Diagnosis Using Deep Neural Networks (CXR-DNNs)"],"prefix":"10.3390","volume":"10","author":[{"given":"Ali Yousuf","family":"Khan","sequence":"first","affiliation":[{"name":"Telecommunications Engineering School, University of Malaga, 29010 Malaga, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9287-2329","authenticated-orcid":false,"given":"Miguel-Angel","family":"Luque-Nieto","sequence":"additional","affiliation":[{"name":"Institute of Oceanic Engineering Research, University of Malaga, 29010 Malaga, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9138-9290","authenticated-orcid":false,"given":"Muhammad Imran","family":"Saleem","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Sir Syed University of Engineering & Technology, Karachi 75300, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7817-6442","authenticated-orcid":false,"given":"Enrique","family":"Nava-Baro","sequence":"additional","affiliation":[{"name":"Institute of Oceanic Engineering Research, University of Malaga, 29010 Malaga, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"D708","DOI":"10.1093\/nar\/gkx932","article-title":"Virus taxonomy: The database of the international committee on taxonomy of viruses (ICTV)","volume":"46","author":"Lefkowitz","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/RBME.2020.2990959","article-title":"The role of imaging in the detection and management of COVID-19: A review","volume":"14","author":"Dong","year":"2021","journal-title":"IEEE Rev. 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