PolypSeg-GradCAM: Towards Explainable Computer-Aided Gastrointestinal Disease Detection Using U-Net Based Segmentation and Grad-CAM Visualization on the Kvasir Dataset
This addresses the need for explainable AI in clinical colonoscopy to improve early colorectal cancer detection, though it is incremental by combining existing methods.
The study tackled the problem of limited interpretability in deep learning for polyp segmentation by integrating U-Net with Grad-CAM, achieving a mean IoU of 0.9257 on the Kvasir-SEG dataset.
Colorectal cancer (CRC) remains one of the leading causes of cancer-related morbidity and mortality worldwide, with gastrointestinal (GI) polyps serving as critical precursors according to the World Health Organization (WHO). Early and accurate segmentation of polyps during colonoscopy is essential for reducing CRC progression, yet manual delineation is labor-intensive and prone to observer variability. Deep learning methods have demonstrated strong potential for automated polyp analysis, but their limited interpretability remains a barrier to clinical adoption. In this study, we present PolypSeg-GradCAM, an explainable deep learning framework that integrates the U-Net architecture with Gradient-weighted Class Activation Mapping (Grad-CAM) for transparent polyp segmentation. The model was trained and evaluated on the Kvasir-SEG dataset of 1000 annotated endoscopic images. Experimental results demonstrate robust segmentation performance, achieving a mean Intersection over Union (IoU) of 0.9257 on the test set and consistently high Dice coefficients (F-score > 0.96) on training and validation sets. Grad-CAM visualizations further confirmed that predictions were guided by clinically relevant regions, enhancing transparency and trust in the model's decisions. By coupling high segmentation accuracy with interpretability, PolypSeg-GradCAM represents a step toward reliable, trustworthy AI-assisted colonoscopy and improved early colorectal cancer prevention.