2MGAS-Net: multi-level multi-scale gated attentional squeezed network for polyp segmentation

Accurate segmentation of colon polyps in endoscopic images is crucial for early colorectal cancer diagnosis and treatment planning. However, achieving this is particularly challenging due to the diverse characteristics of polyps, including variations in size, color, shape, position, boundary ambiguity, and complex structure. To address these challenges, this paper introduces the Multi-level Multi-scale Gated Attentional Squeezed Network (2MGAS-Net), a robust deep learning model designed specifically for polyp segmentation. 2MGAS-Net incorporates a novel modular Multi-scale Gated Attentional Squeezed Feature Fusion (MGAS2F) strategy. MGAS2F effectively captures contextual information at multiple scales through a combination of Multi-scale Squeezed Feature Fusion (MS2F) and Cascaded Gated Attentional Transformer (CGA-T) modules. MS2F enhances the model’s ability to extract detailed polyp features, while CGA-T guides the model for accurate polyp boundary estimation. Experiments on publicly available datasets demonstrate that 2MGAS-Net outperforms existing state-of-the-art methods. This indicates its potential to improve polyp segmentation accuracy significantly, facilitating more accurate clinical decision-making and potentially revolutionizing diagnostic approaches for colorectal cancer.