1.College of Public Health，Xinjiang Medical University，Urumqi 830011，China； 2.College of Medical Engineering Technology，Xinjiang Medical University，Urumqi 830011，China
Abstract： In order to improve the screening and diagnosis efficiency of hepatic hydatid disease, and make up for the shortage of medical resources in some areas, this paper proposes an intelligent typing method of hepatic hydatid disease based on Swin Transformer, which combines the convolution attention mechanism model, and realizes the automatic classification of five types of cystic hydatid disease by learning the whole and local details of images. In order to verify the superiority of the model, the prediction model proposed in this paper is compared with common classification models. The results show that the classification accuracy based on the improved Swin Transformer model can reach 92.6% on the test set. The experimental results show that compared with other algorithms, the improved Swin Transformer network can better classify the ultrasonic images of hepatic cystic echinococcosis, and this method can be extended to other medical applications.
Key words : deep learning；image classification；hepatic cystic echinococcosis；ultrasonic image；transfer learning
