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<p>Skin cancer poses a significant threat to life, necessitating early detection. Skin lesion segmentation, a critical step in diagnosis, remains challenging due to variations in lesion size and edge blurring. Despite recent advancements in computational efficiency, edge detection accuracy remains a bottleneck. In this paper, we propose a lightweight UNet with multi-module synergy and dual-domain attention for precise skin lesion segmentation to address these issues. Our model combines the Swin Transformer (Swin-T) block, Multi-Axis External Weighting (MEWB), Group multi-axis Hadamard Product Attention (GHPA), and Group Aggregation Bridge (GAB) within a lightweight framework. Swin-T reduces complexity through parallel processing, MEWB incorporates frequency domain information for comprehensive feature capture, GHPA extracts pathological information from diverse perspectives, and GAB enhances multi-scale information extraction. On the ISIC2017 and ISIC2018 datasets, our model achieves mIoU and DSC scores of 81.22% and 89.64%, and 81.65% and 89.90%, respectively. These results demonstrate improved segmentation accuracy with low parameter count and computational cost, aiding physicians in diagnosis and treatment. <a href="https://github.com/SolitudeWolf/ESMDL-UNet.git">https://github.com/SolitudeWolf/ESMDL-UNet.git</a></p>

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Lightweight UNet with multi-module synergy and dual-domain attention for precise skin lesion segmentation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月4日

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  • Changhua Chen,
  • Ling Li,
  • Bo Li,
  • Haijun Li,
  • Yanjie You,
  • Wei Zhou,
  • Yuyan Bin,
  • Zhuo Wang,
  • Jiayi Li,
  • Chao Zhang

摘要

Skin cancer poses a significant threat to life, necessitating early detection. Skin lesion segmentation, a critical step in diagnosis, remains challenging due to variations in lesion size and edge blurring. Despite recent advancements in computational efficiency, edge detection accuracy remains a bottleneck. In this paper, we propose a lightweight UNet with multi-module synergy and dual-domain attention for precise skin lesion segmentation to address these issues. Our model combines the Swin Transformer (Swin-T) block, Multi-Axis External Weighting (MEWB), Group multi-axis Hadamard Product Attention (GHPA), and Group Aggregation Bridge (GAB) within a lightweight framework. Swin-T reduces complexity through parallel processing, MEWB incorporates frequency domain information for comprehensive feature capture, GHPA extracts pathological information from diverse perspectives, and GAB enhances multi-scale information extraction. On the ISIC2017 and ISIC2018 datasets, our model achieves mIoU and DSC scores of 81.22% and 89.64%, and 81.65% and 89.90%, respectively. These results demonstrate improved segmentation accuracy with low parameter count and computational cost, aiding physicians in diagnosis and treatment. https://github.com/SolitudeWolf/ESMDL-UNet.git