Automatic Diagnosis of Coronavirus Using Conditional Generative Adversarial Network (CGAN) - Majid, Haneen; Ali, Khawla Hussein (Iraqi Journal of Science, 2023)
A global pandemic has emerged as a result of the widespread coronavirus disease (COVID-19). Deep learning (DL) techniques are used to diagnose COVID-19 based on many chest X-ray. Due to the scarcity of available X-ray images, the performance of DL for COVID-19 detection is lagging, underdeveloped, and suffering from overfitting. Overfitting happens when a network trains a function with an  incredibly high variance to represent the training data perfectly. Consequently, medical images lack the availability of large labeled datasets, and the annotation of medical images is expensive and time-consuming for experts. As the COVID-19 virus is an infectious disease, these datasets are scarce, and it is difficult to get large datasets due to patient privacy. To address these issues by augmenting the COVID-19 dataset. In this paper, we adjusted conditional generation adversarial networks (CGAN) along with traditional augmentation (TA). The augmented dataset includes 6550 X-ray images that can be used to improve the diagnosis of COVID-19, and we have implemented five models of transfer learning procedures (DTL). The proposed procedures yielded high detection accuracy of 95%, 93%, 92%, and 92% in only ten epochs, for VGG-16, VGG-19, Xception, and Inception, respectively, and a custom convolutional neural network. Experimental results prove that our model achieves a high detection accuracy of up to 96% compared to other models. We hope it can be applied in other fields with rare data sets.