Research on Colorectal Polyp Semantic Segmentation Based on E-UNet Network
Keywords:
Colorectal Polyp, Semantic Segmentation, Pyramidal Convolution, Soft Pooling, Improved CBAMAbstract
This study proposes an enhanced computer network named E-UNet, designed to improve the semantic segmentation capability for colorectal polyps. Addressing challenges in colorectal polyp images such as immense scale variations, blurry boundaries, and color similarity to the background, E-UNet makes key improvements based on the UNet architecture. First, Pyramidal Convolution (PyConv) is adopted to replace standard convolution, solving the problem of multi-scale feature extraction without increasing computational cost. Second, Soft Pooling is introduced to replace Max Pooling, reducing information loss during down-sampling and preserving more key low-amplitude signals such as blurry boundaries and subtle textures. Finally, an improved CBAM (I-CBAM) attention mechanism is designed. By processing channel and spatial attention in parallel and optimizing the MLP structure, it dynamically focuses on polyp morphology and key features, thereby overcoming issues such as the similarity between polyp and background colors. Experimental results on the authoritative public dataset Kvasir-SEG show that E-UNet outperforms mainstream methods such as UNet, UNet++, and Attention UNet across all evaluation metrics, achieving an mIoU of 87.5%, an mRecall of 91.9%, and an mPrecision of 93.1%. Ablation studies further verify the effectiveness of the I-CBAM, PyConv, and Soft Pooling modules, with the complete model achieving a 3.7% mIoU improvement compared to the baseline UNet.
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