ADPNet: Attention-Driven Dual-Path Network for automated polyp segmentation in colonoscopy

Accurate automated polyp segmentation in colonoscopy images is crucial for early colorectal cancer detection and treatment, a major global health concern. Effective segmentation aids clinical decision-making and surgical planning. Leveraging advancements in deep learning, we propose an Attention-Driven Dual-Path Network (ADPNet) for precise polyp segmentation. ADPNet features a novel architecture with a specialized bridge integrating the Atrous Self-Attention Pyramid Module (ASAPM) and Dilated Convolution-Transformer Module (DCTM) between the encoder and decoder, enabling efficient feature extraction, long-range dependency capture, and enriched semantic representation. The decoder employs pixel shuffle, gated attention mechanisms, and residual blocks to enhance contextual and spatial feature refinement, ensuring precise boundary delineation and noise suppression. Comprehensive evaluations on public polyp datasets show ADPNet outperforms state-of-the-art models, demonstrating superior accuracy and robustness, particularly in challenging scenarios such as small or concealed polyps. ADPNet offers a robust solution for automated polyp segmentation, with potential to revolutionize early colorectal cancer detection and improve clinical outcomes. The code and results of this article are publicly available at https://github.com/Mkhan143/ADPNet.