Automatic Detection of Knee Osteoarthritis Disease with the Developed CNN, NCA and SVM Based Hybrid Model

Knee osteoarthritis (Knee-OA) is one of the most common musculoskeletal diseases caused by loss of cartilage and bone changes in the joint. Prediction of early Knee-OA based on early bone tissue analysis is challenging in medical image analysis. If the disease is detected in the later stages, it may cause serious problems, such as the need for knee replacement. Therefore, the detection of Knee-OA disease is essential. With the developing technology, computer-aided systems have been frequently used in the biomedical field in recent years. A deep learning-based hybrid model for the early diagnosis and treatment of Knee-OA disease was developed in this study. In the developed hybrid model, three different CNN architectures were used as the base, and feature extraction was made with these architectures. The features obtained in three different architectures are combined to bring together different features of the same image. After merging, the neighboring component analysis (NCA) size reduction method was used to remove unnecessary features. Since unnecessary features are eliminated from the feature map optimized with NCA, the proposed hybrid model will work faster and produce more successful results. Finally, the feature map optimized with NCA was classified with six different classifiers. The proposed model was also compared to eight different CNN architectures. In comparison to CNN architectures, the proposed hybrid model achieved the highest accuracy performance.