A COMPREHENSIVE ANALYSIS OF DEEP LEARNING TECHNIQUES FOR CLASSIFYING KNEE ABNORMALITIES

Knee abnormalities represent one of the most common orthopedic conditions affecting individuals across different age groups, significantly impacting mobility and quality of life. For treatment planning to be successful, these anomalies must be diagnosed promptly and accurately. Deep learning techniques have transformed medical image analysis in the last ten years, providing promising answers for automated knee anomaly classification from a variety of imaging modalities. This comprehensive review examines the current state-of-the-art deep learning techniques for knee abnormality classification, analyzing their architectures, performance metrics, clinical applications, and limitations. We systematically categorize these approaches based on the imaging modalities used (MRI, X-ray, ultrasound), the specific knee abnormalities targeted, and the underlying deep learning architectures employed. Additionally, we discuss the challenges in this field, including limited dataset availability, class imbalance, interpretability issues, and the gap between research and clinical implementation. Finally, we highlight emerging trends and future research directions that could further enhance the clinical utility of deep learning for knee abnormality classification.