Table 4.

AI Models Used in Medical Imaging.

AI Model	Application	Advantages	Limitations
U-Net [92]	Tumor Segmentation	High accuracy in delineation	Requires large datasets
GAN	Image Augmentation	Effective in improving model performance	Computationally intensive
VGGNet [93]	Image Classification	Strong feature extraction	Deep architecture, Overfitting risk
ResNet [94]	Image Classification	Addresses vanishing gradient problem	Complexity increases with depth
DenseNet [95]	Lesion Detection	Efficient feature reuse	High memory consumption
YOLO [96]	Object Detection	Real-time processing capability	Less accuracy for small objects
Xception [97]	Disease Classification	Depthwise separable convolutions for efficiency	Requires extensive tuning
MobileNet [98]	Mobile Imaging Applications	Lightweight and fast for mobile devices	Lower accuracy compared to larger models
Faster R-CNN [99]	Tumor Detection	High accuracy in detection	Slower than single-shot models