UNet and Swin Transformer Fusion Network for Lesion Segmentation in Biological Kidney Imaging

We research the segmentation of lesions in medical images using the PST-UNet model, verifying the preservation of spatial features of medical lesions, and improving the accuracy of medical lesion segmentation. The PST-UNet (positive distribution data Swin transformer) model combines transformer and U-shaped structures. It uses cascaded convolution fusion modules to integrate the encoder’s multi-scale features. The encoder includes the Swin transformer block and the entire Gaussian Error Linear Unit (GELU) activation function. The decoder uses the Swin transformer block, upsampling, and skip connections from the cascaded convolution fusion modules. This approach effectively preserves more spatial features of medical lesions and improves the accuracy of kidney lesion segmentation, achieving a 0.02289 improvement in accuracy. The PST-UNet model can improve the segmentation accuracy of normally distributed medical lesion data, which has a positive effect on the treatment of kidney disease.