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

Abstract

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.

Introduction

1. Overview

• Colorectal cancer (CRC) is a leading global cause of cancer-related deaths.

• Early detection is critical to improving survival, with medical imaging (colonoscopy, CT, MRI) playing a key role.

• Accurate segmentation of cancerous regions is essential for diagnosis and treatment.

2. Challenges in CRC Image Segmentation

• Traditional methods using handcrafted features (intensity, texture, shape) lack robustness across large and diverse datasets.

• Tumor irregularities, low contrast, and imaging variations reduce segmentation accuracy.

• Deep learning models, especially U-Net and its variants, have improved results but require large annotated datasets.

3. Advancements in Deep Learning and GANs

• CNN-based U-Net revolutionized biomedical segmentation with encoder–decoder and skip connections.

• Enhanced variants:

  • UNet++: Dense connections improve small tumor detection.

  • Attention U-Net: Uses attention mechanisms to focus on tumors.

  • PraNet: Designed for polyp segmentation using boundary refinement.

  • HarDNet-MSEG: Lightweight, real-time segmentation model.

  • TransUNet: Combines CNN and transformers for global context capture.

• GANs (e.g., Pix2Pix, CycleGAN) introduced adversarial learning for both segmentation and data generation but suffer from training instability.

4. Optimization with Metaheuristics: Sine Cosine Algorithm (SCA)

• GANs are sensitive to hyperparameters and prone to mode collapse or unstable training.

• The Sine Cosine Algorithm (SCA):

  • Optimizes GAN parameters (learning rate, batch size, etc.).

  • Stabilizes training.

  • Enhances segmentation accuracy and convergence speed.

  • Reduces the need for manual tuning.

5. Proposed Methodology

• Combines Attention U-Net (Generator) and PatchGAN (Discriminator) in a GAN setup.

• SCA tunes hyperparameters dynamically for balanced training.

• Steps:

  1. Preprocessing of colonoscopy images to enhance lesion visibility.

  2. Segmentation via adversarial training.

  3. Synthetic data generation to combat annotation scarcity.

  4. Training on benchmark datasets with augmentation.

  5. Evaluation using Dice Similarity Coefficient (DSC), IoU, sensitivity, specificity.

6. Literature Highlights

7. Future Directions

• Hybrid Transformer-GAN models for better accuracy.

• Explore alternative optimizers (e.g., PSO, GA) to replace or enhance SCA.

• Develop large synthetic datasets to minimize manual labeling.

• Use federated learning for privacy-preserving GAN training across institutions.

• Improve cross-dataset generalization for real-world clinical use.

• Integrate multi-modal imaging (MRI, CT, colonoscopy).

• Employ semi- and self-supervised GANs to leverage unlabelled data.

• Combine with Explainable AI (XAI) to increase clinical trust.

Conclusion

The Colon cancer remains a major health concern worldwide, where accurate segmentation of polyps and tumors plays a vital role in early detection and effective treatment. Advances in deep learning, particularly U-Net variants and Transformer-based models, have significantly improved segmentation performance. More recently, Generative Adversarial Networks (GANs) have shown remarkable potential in both boosting segmentation accuracy and generating realistic synthetic data to overcome dataset limitations. Optimization methods, such as the Sine Cosine Algorithm, further strengthen GAN stability and efficiency. Looking ahead, the integration of GANs with emerging deep learning paradigms offers promising pathways toward more reliable, generalizable, and clinically useful computer-aided diagnosis.

References

[1] Ronneberger O., Fischer P., Brox T. “U-Net: Convolutional Networks for Biomedical Image Segmentation,” 2015. [2] Zhou Z., Siddiquee M., Tajbakhsh N., Liang J. “UNet++: A Nested U-Net Architecture for Medical Image Segmentation,” 2018. [3] Oktay O. et al. “Attention U-Net: Learning Where to Look for the Pancreas,” 2018. [4] Fan D.-P. et al. “PraNet: Parallel Reverse Attention Network for Polyp Segmentation,” 2020. [5] Huang H. et al. “HarDNet-MSEG: A Lightweight Model for Real-time Polyp Segmentation,” 2021. [6] Chen J. et al. “TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation,” 2021. [7] Isola P. et al. “Image-to-Image Translation with Conditional Adversarial Networks (Pix2Pix),” 2017. [8] Zhu J.-Y. et al. “Unpaired Image-to-Image Translation using CycleGAN,” 2017. [9] Wang R. et al. “Semi-Supervised Colon Polyp Segmentation with Adversarial Learning,” 2019. [10] Mirjalili S. “SCA: Sine Cosine Algorithm for Global Optimization,” 2016.

Copyright

Copyright © 2025 Anuradha Badage, Anusha D Walikar, Deepika -, Himavarshini V, Hinduja R P. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.