{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T02:28:36Z","timestamp":1775701716902,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:00:00Z","timestamp":1705536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universiti Malaysia Pahang Al-Sultan Abdullah","award":["RDU230375"],"award-info":[{"award-number":["RDU230375"]}]},{"name":"Universiti Malaysia Pahang Al-Sultan Abdullah","award":["PGRS220301"],"award-info":[{"award-number":["PGRS220301"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Convolutional Neural Networks (CNNs) have garnered significant utilisation within automated image classification systems. CNNs possess the ability to leverage the spatial and temporal correlations inherent in a dataset. This study delves into the use of cutting-edge deep learning for precise image data classification, focusing on overcoming the difficulties brought on by the COVID-19 pandemic. In order to improve the accuracy and robustness of COVID-19 image classification, the study introduces a novel methodology that combines the strength of Deep Convolutional Neural Networks (DCNNs) and Generative Adversarial Networks (GANs). This proposed study helps to mitigate the lack of labelled coronavirus (COVID-19) images, which has been a standard limitation in related research, and improves the model\u2019s ability to distinguish between COVID-19-related patterns and healthy lung images. The study uses a thorough case study and uses a sizable dataset of chest X-ray images covering COVID-19 cases, other respiratory conditions, and healthy lung conditions. The integrated model outperforms conventional DCNN-based techniques in terms of classification accuracy after being trained on this dataset. To address the issues of an unbalanced dataset, GAN will produce synthetic pictures and extract deep features from every image. A thorough understanding of the model\u2019s performance in real-world scenarios is also provided by the study\u2019s meticulous evaluation of the model\u2019s performance using a variety of metrics, including accuracy, precision, recall, and F1-score.<\/jats:p>","DOI":"10.3390\/info15010058","type":"journal-article","created":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T11:28:46Z","timestamp":1705577326000},"page":"58","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Integrated Generative Adversarial Networks and Deep Convolutional Neural Networks for Image Data Classification: A Case Study for COVID-19"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0501-0737","authenticated-orcid":false,"given":"Ku Muhammad Naim Ku","family":"Khalif","sequence":"first","affiliation":[{"name":"Centre for Mathematical Sciences, Universiti Malaysia Pahang Al-Sultan Abdullah, Kuantan 23600, Malaysia"},{"name":"Centre of Excellence for Artificial Intelligence & Data Science, Universiti Malaysia Pahang Al-Sultan Abdullah, Kuantan 23600, Malaysia"}]},{"given":"Woo","family":"Chaw Seng","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6166-296X","authenticated-orcid":false,"given":"Alexander","family":"Gegov","sequence":"additional","affiliation":[{"name":"School of Computing, Faculty of Technology, University of Portsmouth, Portsmouth PO1 3HE, UK"},{"name":"English Faculty of Engineering, Technical University of Sofia, 1756 Sofia, Bulgaria"}]},{"given":"Ahmad Syafadhli Abu","family":"Bakar","sequence":"additional","affiliation":[{"name":"Mathematics Division, Centre for Foundation Studies in Science, University of Malaya, Kuala Lumpur 50603, Malaysia"},{"name":"Centre of Research for Computational Sciences and Informatics in Biology, Bioindustry, Environment, Agriculture and Healthcare (CRYSTAL), University of Malaya, Kuala Lumpur 50603, Malaysia"}]},{"given":"Nur Adibah","family":"Shahrul","sequence":"additional","affiliation":[{"name":"Negeri Sembilan State Health Department, Ministry of Health Malaysia, Seremban 70300, Malaysia"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3609","DOI":"10.1007\/s12652-021-03488-z","article-title":"Transfer learning for image classification using VGG19: Caltech-101 image data set","volume":"14","author":"Bansal","year":"2023","journal-title":"J. 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