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.
Chen et al. (Thu,) studied this question.