Table 8.

The methods, properties, and features of GAN-COVID-19 mechanisms.

Authors	Main idea	Advantages	Research challenges	Security mechanism?	Dataset	Using TL?	Method	Usage?
Goel, Murugan [72]	Using the WOA approach to optimize GAN's hyperparameters.	-Specificity 97.78%, sensitivity 99.78%,	-Vanishing gradients	No	There are 2482 COVID-19 CT scan images in the dataset, with 1252 COVID-19 and 1230 non-COVID-19 images.	No	GAN + The Whale optimization	Detection in chest CT
		F1-score 98.79%, accuracy 99.22%, positive predictive value 97.82%, and negative predictive value 99.77%.	mode collapse instability.				algorithm
Rasheed, Hameed [73]	Using GAN to increase the number of training samples and reduce the issue of overfitting.	-Positive cases recognition accuracy ranges from 95.2 to 97.6% without PCA and 97.6–100% with PCA.	-High complexity	No	GAN was used to generate 500 X-ray images from an online dataset.	No	GAN + logistic regression	Detection in chest X-ray
			-High energy consumption
Elzeki, Shams [74]	Proposing a lightweight architecture based on a single completely connected layer representing the critical features	The accuracy was 96.7% for two classes, and for three classes, it was 93.07%, with the model's overall accuracy being 94.5%.	-Different modified cases of the COVID-19 X-Ray image must be studied.	No	COVID-19 X-Ray datasets from three separate sources.	No	GNN	Detection in chest X-ray
Singh, Pandey [75]	Proposing a GAN-based approach to assist in the quicker triage of COVID-19 patients, thus reducing the risk of human error.	−98.67% accuracy, 0.98 Kappa score, and F-1 scores of 100, 98, and 98, respectively.	-The size of the training data set must be increased.	No	The open-source NIH chest X-ray dataset used in the RSNA pneumonia detection challenge on Kaggle was used.	No	GAN	Detection in chest X-ray
		-Low energy consumption	-High delay