ESFCU-Net: A Lightweight Hybrid Architecture Incorporating Self-Attention and Edge Enhancement Mechanisms for Enhanced Polyp Image Segmentation

Early detection of polyps during endoscopy reduces the risk of malignancy and facilitates timely intervention. Precise polyp segmentation during endoscopy aids clinicians in identifying polyps, playing a vital role in the clinical prevention of malignancy. However, due to considerable differences in the size, color, and morphology of polyps, the resemblance between polyp lesions and their background, and the impact of factors like lighting changes, low-contrast areas, and gastrointestinal contents during image acquisition, accurate polyp segmentation remains a challenging issue. Additionally, most existing methods require high computational power, which restricts their practical application. Our objective is to develop and test a new lightweight polyp segmentation architecture. This paper presents a hybrid lightweight architecture called ESFCU-Net that combines self-attention and edge enhancement to address these challenges. The model comprises an encoder-decoder and an improved fire module (ESF module), which can learn both local and global information, reduce information loss, maintain computational efficiency, enhance the extraction of critical features in images, and includes a coordinate attention mechanism in each skip connection to suppress background interference and minimize spatial information loss. Extensive validation on two public datasets (Kvasir-SEG and CVC-ClinicDB) and one internal dataset reveals that this network exhibits strong learning performance and generalization capabilities, significantly enhances segmentation accuracy, surpasses existing segmentation methods, and shows potential for clinical application. The code for our work and more technical details can be found at https://github.com/aaafoxy/ESFCU-Net.git.