Automated Detection and Severity Grading of Knee Osteoarthritis from X-ray Images Using Machine Learning with Detailed Literature Review

Abstract

A groundbreaking approach to the detection and severity grading of knee osteoarthritis utilizing X-ray imaging and a Convolutional Neural Network (CNN) architecture, specifically EfficientNet B5. Osteoarthritis, a prevalent degenerative joint disease, requires accurate and timely diagnosis for effective management. The proposed methodology focuses on enhancing diagnostic accuracy through advanced deep-learning techniques. The study leverages a diverse dataset of knee X-ray images sourced from various clinical settings, encompassing a spectrum of OA severity levels. The EfficientNet B5 architecture, known for its superior performance and efficiency, is employed for feature extraction and classification. The CNN model is trained and validated on a definite dataset, achieving an impressive accuracy of 95.84% in differentiating knee osteoarthritis from normal conditions to multiple grades like Healthy, Minimal, Doubtful, Moderate, and Severe. Further enhances clinical utility and incorporates a severity grading mechanism, providing clinicians with a detailed assessment of the disease progression. The severity grading is accomplished through fine-tuning the CNN model on a sub-dataset with annotated severity levels. The integration of severity grading demonstrates the model’s ability to detect knee osteoarthritis accurately and quantify its severity with high precision. The findings of this research highlight the potential of Efficient Net B5- based CNNs as a robust and efficient tool for knee osteoarthritis diagnosis and severity grading. The reported accuracy of 95.84% positions the proposed model as a promising asset in clinical settings, offering a reliable and automated solution for early detection and precise evaluation of knee osteoarthritis. This research, with its potential to significantly improve the diagnostic capabilities of musculoskeletal disorders, ultimately enhances patient care and outcomes by providing a more accurate and timely diagnosis.