Automated grading of knee osteoarthritis X-ray images based on attention mechanism
Yibo Feng, Jing Liu, Huan Zhang, Dawei Qiu
Abstract
Knee osteoarthritis (OA) is a common skeletal muscle disease with a high incidence in the elderly. Common symptoms of knee OA include popping, swelling, and fluid accumulation. Certain serious cases may experience joint deformities. The accuracy of clinical diagnosis often depends on the subjective experience of radiologists. Computer-aided diagnosis can effectively reduce the workload of radiologists and improve the diagnostic efficiency. In this paper, a channel attention module and spatial attention module are introduced to enhance the utilization of effective information and the suppression of unwanted information. The merged results of the double branches in the attention modules were compressed and transformed. The Mish activation function was introduced to enhance the stability of the network and enable the network to converge quickly. Training and testing were performed on a knee OA dataset, and the proposed method achieved an overall accuracy, recall, precision and F1 scores of 70.23, 68.23, 70.25, and 67.55% respectively, demonstrating that the network could achieve state-of-the-art results on the knee OA dataset. A gradient-weighted class activation-mapping algorithm was applied to visualize the output results.