Ku Muhammad Naim, Ku Khalif and Seng, Woo Chaw and Gegov, Alexander and Ahmad Syafadhli, Abu Bakar and Nur Adibah, Shahrul (2024) Integrated Generative Adversarial Networks and Deep Convolutional Neural Networks for Image Data Classification A Case Study for COVID-19. Information, 15 (1). ISSN 2078-2489. (Published)
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Abstract
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’s 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’s performance in real-world scenarios is also provided by the study’s meticulous evaluation of the model’s performance using a variety of metrics, including accuracy, precision, recall, and F1-score.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | generative adversarial networks; deep convolutional neural network; VGG16; COVID-19 |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Faculty/Division: | Center for Mathematical Science Centre of Excellence for Artificial Intelligence & Data Science |
| Depositing User: | Miss Amelia Binti Hasan |
| Date Deposited: | 24 Jan 2024 04:24 |
| Last Modified: | 24 Jan 2024 04:24 |
| URI: | http://umpir.ump.edu.my/id/eprint/40169 |
| Download Statistic: | View Download Statistics |
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