LGPS: A lightweight GAN-based approach for polyp segmentation in colonoscopy images

Accurate and real-time polyp segmentation is essential for early colorectal cancer detection, yet remains challenging due to small, low-contrast lesions and the computational demands of many deep learning models. We propose LGPS, a Lightweight GAN-based Polyp Segmentation framework that achieves high segmentation accuracy with minimal computational overhead. LGPS integrates a MobileNetV2-based generator enhanced with Residual Squeeze-and-Excitation (ReSE) blocks and a discriminator equipped with Convolutional Conditional Random Fields (ConvCRF) to refine spatial coherence and boundary precision. A hybrid loss function — combining Binary Cross-Entropy, Weighted IoU, and Dice Loss — improves class imbalance handling and sensitivity to small or blurry polyps. LGPS is an extremely compact model, requiring only 1.07 million parameters — over 17 × smaller than many recent transformer- and CNN-based SOTA architectures — while preserving high segmentation accuracy. In quantitative evaluation, LGPS achieves a Dice score of 0.7299 and an IoU of 0.7867 on the multi-center PolypGen dataset, the most challenging benchmark in polyp segmentation. On the widely used CVC-ClinicDB dataset, LGPS attains the highest IoU (0.9238) among lightweight and transformer-based approaches. The model also runs at 100.08 FPS on 256 × 256 inputs, demonstrating true real-time capability. Stratified evaluation and qualitative results further confirm its robustness on small and low-contrast lesions. These results highlight LGPS as a computationally efficient yet high-performing framework suitable for real-time clinical deployment. The code is available at https://github.com/Falmi/LGPS/ . • Introduces LGPS, a lightweight GAN-based model for real-time polyp segmentation. • Integrates MobileNetV2 with ReSE blocks and a ConvCRF discriminator for refined predictions. • Proposes a novel hybrid loss (BCE + WIoU + Dice) to enhance generalization to small or low-contrast polyps. • Achieves 0.7299 Dice on PolypGen and 0.9238 IoU on CVC-ClinicDB with only 1.07M parameters. • Delivers real-time performance at over 100 FPS, making it suitable for deployment in clinical endoscopy systems.