Fusion Models of Transfer Learning and Enhanced Learning for Early Diagnosis of Joint Diseases

The early diagnosis of joint diseases has always been a hot issue in the medical field. The model based on the combination of migration learning and enhanced learning can provide new ideas for clinical diagnosis and treatment. This article studied a method that combined migration learning and enhanced learning, making full use of existing knowledge and experience to achieve early diagnosis of joint diseases, and on this basis, through interaction with the environment, to achieve early diagnosis of joint diseases. The research content of the article includes: data preprocessing, migration learning, enhanced learning, fusion modeling, model training and evaluation. On this basis, the article used techniques such as feature expression and strategy selection in migration learning to effectively identify joint diseases, so as to provide effective characteristic information for the diagnosis of joint diseases and make behavioral decisions on them. This method is expected to realize the early diagnosis of joint diseases and improve the accuracy and efficiency of diagnosis. The two methods have reached 95.8% and 89.5% respectively.