To improve the segmentation accuracy of tumor regions and reduce computational complexity in breast ultrasound images

a segmentation model FLA-CSWin-U-Net that integrates cross-shaped window Transformer(CSWin Transformer)with focused linear attention (FLA)is proposed.This method adopts U-Net as its basic architecture.The encoder utilizes an improved focused linear attention cross-shaped window Transformer（FLA-CSWin Transformer）module

which enhances the global context modeling capability.A focused linear attention mechanism is introduced to enhance feature interaction in key regions while maintaining linear computational complexity.The decoder employs dynamic upsampling(DySample)operator to improve the efficiency of detail restoration.Experimental results on the public BUSI dataset demonstrate that the proposed model achieves a Dice coefficient of 94.3%

which is an improvement of 11.07% over the traditional U-Net.With a parameter count of 23.06M and a computational load reduced to 4.09 GFLOPs

the model significantly enhances segmentation effect of blurred boundaries and small lesions

and has high clinical practical value and deployment feasibility.