Table 2.

Overview of GAN-based methods.

Ref.	Approach	Findings	Metrics
[68]	Capsule network GAN+LeNet	Prostate image classification	Accuracy 89.20%
[69]	GAN+AlexNet	Parkinson's disease	Accuracy 89.23%
[70]	GAN+DenseNet121	Skin lesion classification	Accuracy 94.25%
[71]	GAN+InceptionV3	Breast mass classification	Accuracy 90.41%
[72]	GAN+ResNet50	Brain tumor classification	Accuracy 96.25%
[73]	3D U-Net, VGG16	Brain tumor segmentation	DSC 90.1%
[74]	U-Net, fully connected CNN	Breast tumor segmentation	DSC 88.41%
[75]	DeepLapV2 [76], FCN	Left ventricle segmentation	DSC 88.0%
[77]	U-Net, FCN	Whole heart segmentation	DSC 86.32%
[78]	Autoencoder, CNN	Lung lesion segmentation	DSC 62.0%