Osteoarthritis is a common degenerative joint disease, which is often analyzed by X-ray images. However, if there is a lack of clinical experience when reading the films, it is easy to cause misdiagnosis. Although deep learning has made significant progress in the field of medical image processing, existing models still have limitations in capturing subtle lesion features such as joint spaces. This paper proposes an automatic diagnosis method for osteoarthritis based on the improved shifted windows Transformer (Swin Transformer) and graph convolutional network. By enhancing the modeling of joint space features and cross-layer feature fusion, it is expected to effectively improve the accuracy of early diagnosis of osteoarthritis. Firstly, this paper designs the shifted windows horizontal attention mechanism (SW-HAM), which can enhance the feature extraction ability in the horizontal direction. Secondly, the central-attention graphSAGE (CAG-SAGE) is introduced to conduct weighted aggregation of the feature information of the lesion area through the dynamic attention mechanism. Finally, cross-layer connection technology is utilized to achieve efficient fusion of multi-layer features. The experimental results show that the SW-HAM and CAG-SAGE modules and cross-layer connections significantly improve the model performance. The classification accuracy, recall rate, precision rate, F1 score, and area under the curve are 94.59%, 95.14%, 94.05%, 94.41%, and 96.30% respectively, all of which are superior to the classical network and existing methods. It provides a new and effective method for the classification and diagnosis of osteoarthritis.

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