Abstract: To address issues such as blurry skin lesion boundaries,noise caused by hair,incomplete segmentation of lesion regions,and significant differences in lesion feature distribution,this paper proposes CA-SFTNet,a U-Net-based algorithm integrating a condensed attention neural block and residual spatial feature transformation.Firstly,feature segmentation during downsampling preserves shallow semantic lesion information.Secondly,condensed attention neural block in skip connections enhances focus on lesion regions by adaptively weighting critical features.Finally,a residual spatial feature transformation module is integra-ted at the network’s tail,enabling adaptive adjustment for spatially heterogeneous regions and enhancing recognition of lesions with heterogeneous feature distributions.Experiments conducted on the ISIC2017 and ISIC2018 datasets demonstrate that CA-SFTNet outperforms the conventional U-Net in skin lesion segmentation.Specifically,it achieves Dice coefficients of 93.12% and 92.36%,representing improvements of 7.15 and 4.81 percentage points over U-Net,respectively.The corresponding IoU values are 82.59% and 82.31%,which constitute gains of 6.23 and 4.45 percentage points.Moreover,when compared with state-of-the-art Transformer-based architectures such as TransUNet and Swin-UNet,CA-SFTNet consistently improves the Dice coefficient by 2～6 percentage points and the IoU by 1.8～4.0 percentage points.These results collectively demonstrate the superiority of the proposed method in skin lesion segmentation and its effectiveness in enhancing segmentation accuracy.

Key words: Skin lesion, U-Net, Condensed attention neural block, Residual spatial feature transformation, Semantic segmentation