POLYP IMAGE SEGMENTATION USING DEEP LEARNING TECHNIQUES: RESUNET++ ARCHITECTURE

Abstract

This study presents a novel polyp segmentation approach using ResUnet++. Trained on Kvasir-SEG and CVC-ClinicDB, ResUnet++ significantly outperforms traditional UNet and ResUnet. Its residual blocks and attention mechanisms enhance feature extraction, leading to improved segmentation in challenging cases. This highlights the potential of deep learning for advancing polyp segmentation and improving early colorectal cancer detection. Future research could explore further modifications or alternative architectures.