Classification and Detection of Knee Osteoarthritis Using X-Ray Images with Machine Learning Algorithms

Osteoarthritis of the knee is the utmost dominant usage of arthritis, caused by the degeneration of joint tissues. It is characterized by symptoms such as severe pain and reduced mobility, which can be observed through X-ray imaging. Radiographic evidence often includes narrowing of the joint space, formation of bone spurs (osteophytes), tissue degradation, sclerosis, and structural distortions of the bone. Among imaging techniques, plain radiography (X-ray) is the best cost-effective and broadly accessible method for diagnosing knee osteoarthritis. The Kellgren and Lawrence (KL) classification system provides a systematic approach to assess the severity of knee osteoarthritis by grading X-ray images based on the extent of tissue degeneration. This study aimed to enhance the knee osteoarthritis detection and classification using advanced machine learning techniques like KOA based Random Forest classifiers, to improve the accuracy of the KL grading system. The dataset comprised X-ray images of knee joints, which were divided into training and testing subsets in a 80:20 ratios. The proposed ML based KOA-RF approach achieved an impressive accurateness of 87 percentages, demonstrating its effectiveness in detecting and classifying knee osteoarthritis.