| Popis: |
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 down-sampling and up-sampling processing, and then the athlete knee joint injury image segmentation result is output using the end backpropagation layer. The results show that the proposed method has a low error rate in extracting the mask region from the knee joint injury image of athletes, and a short completion time for extracting the mask region, the most detailed and comprehensive segmented athlete knee joint injury image, the maximum error rate of image segmentation is only 6.8%, and the maximum value of segmentation time is only 3.96s. It has important research value in the field of athlete knee joint injury diagnosis. |
