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Machine Learning Methods for Knee Feature Extraction from MR Images

Rheumatoid Arthritis (RA), is considered as a chronic disease that occurs due to progressive changes in the structural properties of knee tissues. In particular, cartilage thickness, bone density, meniscus volume, and synovial fluid composition are the key causes for this health issue that can be identified by magnetic resonance imaging. Therefore, this paper presents a comprehensive study of machine learning techniques for knee feature selection for the diagnosis of rheumatoid arthritis from magnetic resonance images. Over the past decades, these technologies have improved. Despite the many advantages of machine learning for feature selection, they still present some barriers and challenges to overcome. This paper reviews the basic concepts of rheumatoid arthritis, along with machine learning benefits. The paper also discusses the classification strategies and provides researchers with a new vision of feature selection with future trends in machine learning technology.