Tongue body positioning and segmentation based on improved TransUNet

This paper proposes an improved TransUNet medical image segmentation method for tongue positioning and segmentation to solve the premise of tongue image diagnosis task and the segmentation performance of deep learning network. By introducing efficient multi-scale attention module in the jump connection layer of the network, the image conducts deeper feature extraction and compensate for the spatial information lost by image dimension reduction. At the same time, an improved empty space convolution pooling pyramid is added to the convolutional neural layer of the network to enhance the network perception of the input image while reducing the network training cost. Facing the complex background conditions and a variety of interference problems, the network has excellent performance.