This article proposes an orthopedic image recognition method based on an improved convolutional neural network to address the traditional diagnostic methods of orthopedic diseases and the diverse types of orthopedic diseases in the diagnostic process. This method uses a fixed convolutional number to extract key features of orthopedic diseases and reduce the number of features. The article takes the diagnosis of gout in bone diseases as an example and designs an evaluation method based on visualization technology and quantitative indicator calculation. The quantitative indicator calculation obtains the total volume information of urate crystals, thereby assisting doctors in gout diagnosis. The experimental results show that the diagnostic accuracy reaches 98.7%, which can meet the actual diagnostic requirements.
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