A Survey on Precision Colon Cancer Synthesis with Deep GAN-Framework

Colorectal cancer remains one of the leading causes of cancer-related mortality worldwide. Accurate segmentation of polyps and tumors in colonoscopy images is critical for early detection and effective treatment planning. Traditional segmentation methods relied on hand-crafted features, while deep learning methods, particularly U-Net and its variants, have significantly advanced accuracy. More recently, Generative Adversarial Networks (GANs) have been employed for colon cancer segmentation and synthetic data generation, addressing challenges of limited annotated datasets. This survey summarizes key developments in colon cancer segmentation techniques, explores GAN-based advancements, compares state-of-the-art methods, and highlights challenges such as data scarcity, model instability, and generalization. Finally, future research directions are discussed, emphasizing optimization methods, transformer-GAN hybrids, and privacy-preserving learning.