Knee Osteoarthritis Based X- Ray Images Detection and Classification Using RCNN

Osteoarthritis is the most common disease that affects joints, and its presentation mainly occurs among the aged population. Although an appropriate diagnosis of OA has been determinant in avoiding drastic progression in joint deterioration, the presently available radiographic methods depend on personal judgment. This paper proposes a deep learning-based CNN model for the automatic detection and classification of OA stages from knee X-ray images. The model was trained and tested on the OAI dataset and achieved a satisfactory accuracy of 99%. Advanced image preprocessing techniques, such as contrast enhancement and knee joint segmentation, were used to optimize the performance of the model. Extensive evaluation metrics, such as confusion matrices and ROC curves, confirmed the robustness of the proposed model. The results suggest the promise of AI systems in reinforcing clinical decision-making processes, especially in the early detection of OA. We also present ethical considerations within the applications of AI to healthcare, discussing future research directions.