A Deep Learning Framework for Diagnosing and Predicting Knee Osteoarthritis Utilizing Convolutional Neural Networks

Knee osteoarthritis (KOA) is a degenerative disorder that significantly impacts mobility and diminishes the overall quality of life, particularly in individuals who are obese. The disease progression is exacerbated by inflammatory processes involving cytokines and metabolic factors. The irreversible damage caused by KOA often leads to the necessity for total knee replacement (TKR), a procedure that is expensive and has a limited lifespan. Current diagnostic methods heavily rely on manual segmentation and annotation of X-ray images, which are labor-intensive and prone to observer variability. This research proposes a deep learning framework that employs Convolutional Neural Networks (CNNs) to automate the classification of knee X-ray images, enhancing the early and accurate detection of KOA. The Kellgren-Lawrence (KL) grading scale is the most widely accepted method for predicting the severity of KOA based on radiographic evidence from knee X-rays. The dataset utilized in this study comprises $\mathbf{1, 8 0 0}$ digital X-ray images obtained from the global hospitals and diagnostic centers, captured using the PROTEC PRS 500E X-ray machine. Our CNN model achieved a high diagnostic accuracy, demonstrating its potential for practical clinical use. The CNN model undergoes extensive training and validation to achieve high diagnostic accuracy, followed by its integration into a webbased application developed with the Flask framework. This deep learning framework employing CNN frameworks enhances the accuracy of diagnosing and classifying knee osteoarthritis severity, improving early detection, clinical efficiency, and patient outcomes.