Abstract: The boundary of lesions in breast ultrasound images is uncertain and the shape varies.The original U²-Net has a pro-blem with a large number of parameters.In response to this,this paper proposes a lightweight and improved U²-Net breast ultrasound lesion segmentation method called SCF U²-Net,which combines fuzzy logic.It utilizes fuzzy logic to blur the feature map pixels and calculates uncertainty values,then multiply them with the input feature map to reduce image blurriness effectively addressing the uncertainty issue at boundaries.It also improves the Residual U-blocks (RSU) by combining depthwise separable convolution and dilated convolution to reduce model parameter count and enhance segmentation efficiency.To address the varied morphology of breast lesions,embedding coordinate attention mechanisms in the decoding stage to strengthen information extraction capabilities for regions of interest,thereby improving segmentation accuracy.Through testing on BUSI dataset,the proposed method achieves Dice and IoU scores of 0.8975 and 0.8328 respectively,while reducing parameter count by 90% and increasing inference speed by 1.9 times compared to original speeds.Furthermore,when compared with three mainstream semantic segmentation models,the proposed algorithm demonstrates superior performance,has a significantly segmentation performance advantage with a comparable parameters,and has good clinical application value.

Key words: Breast lesion segmentation, Fuzzy logic, Lightweight model, Sep-dilated convolution, Attention mechanism