Intelligent Medical Diagnostic System for Osteoarthritis using Deep Learning

Osteoarthritis (OA) is a prevalent joint disorder, particularly impacting older and overweight individuals, leading to diminished quality of life and increased frailty. This review paper focuses on the current diagnostic methods for OA, which primarily rely on clinical examinations and imaging techniques. However, these approaches may lack efficiency and precision, prompting the need for advanced diagnostic systems. This paper proposes an Intelligent Medical Diagnostic System for Osteoarthritis utilizing deep learning and medical imaging. By integrating deep features with medical images, the system aims to accurately detect and classify OA, particularly in the knee joint. Challenges such as irrelevant feature selection and managing large image datasets are addressed, alongside an exploration of Magnetic Resonance Imaging (MRI) techniques for OA detection and classification. The review provides a comprehensive discussion on location strategies, feature extraction techniques, and classification methods pertinent to OA diagnosis, highlighting recent advancements and future research directions.