Connective tissue that helps the joints and bones is named as Cartilage which is present between the bones. Degradation of the tissues between the bones is known as osteoarthritis (OA). It affects the lots of population worldwide which leads to Ache, Rigidity, and Immobility. Early osteoarthritis detection and categorization are challenging for precise analysis and appropriate treatment. Treatments which are followed earlier are totally depends upon the clinical investigations like radiographic x-ray images which is very time-consuming. Subsequently, these processed images are categorized as normal & osteoarthritis. Our goal is to detect knee osteoarthritis. Latest research on machine learning Artificial intelligence(AI) techniques provide much better solutions for the medical image findings and give better solutions for the analysis of osteoarthritis (OA) using different types of imaging technologies. In order to detect OA, this study focuses on Grey-Level Co-Occurrence Matrix (GLCM) feature extraction in conjunction with ML classifiers, specifically Naïve Bayes, Random Forest, and Decision Tree. The Random Forest classifier reaches an accuracy of 89%, according to experimental results. This will help to improve the quality of life.
Osteoarthritis detection, X-ray images, segmentation, machine learning classifier
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