Image Segmentation Method for Athlete Knee Joint Injury Using Transformer Model by MIoT

Abstract

The segmentation of athlete knee joint injury images can provide doctors with information about the location and extent of the athlete knee joint injury. Therefore, it is significant to segment the images of athlete knee joint injury. However, the traditional image segmentation method of athlete knee injury has the problems of low accuracy of mask region extraction, completion time of extraction and high error rate of segmentation. In the paper, we propose image segmentation method for athlete knee joint injury using the transformer model by the medical Internet of Things (MIoT). First, the MIoT was used as a way to obtain images of knee joint injury of athletes, and the images of knee joint injury of athletes were derived using the MIoT. Second, the exported image is input into the shadow expansion layer of the transformer model, which performs shadow expansion on the athlete knee joint injury image to obtain its mask region, and then the image is input into the patch embedding layer. Finally, after the patch embedding layer extracts the mask patch of the athlete knee joint injury image, the mask patch is input into the transformer block for downsampling and up-sampling processing, and then the athlete knee joint injury image segmentation result is HIGHLIGHTS• An image segmentation for athlete knee joint injury using the transformer model is proposed.• Reducing the complexity of transformer model training.• The comprehensive performance of this paper's method is verified by several evaluation criteria.