Publication

Abstract : This study presents a novel computer-aided diagnostic (CAD) system for detecting and grading the severity of knee osteoarthritis(KOA) from X-ray images, utilizing a hybrid deep learning and machine learning framework. The system combines YOLOv5 for precise knee joint localization and segmentation with a Random Forest classifier for ordinal Kellgren-Lawrence (KL) grading. Trained on a curated and augmented dataset of 1535 X-ray images, the model achieves an overall KL grading accuracy of 87 %. Evaluation includes ROC-AUC curves, Cohen’s kappa scores, and grade-wise sensitivity and specificity metrics. This hybrid approach offers a scalable, interpretable, and clinically relevant tool for supporting radiologists in early KOA diagnosis, especially in resource-constrained settings. • Combines the powerful feature extraction capabilities of the YOLOv5 deep learning architecture with the classification strength of the Random Forest model. • YOLOv5 is used for knee joint segmentation to reduce background noise and improve classifier accuracy by focusing on the region of interest. • Achieves 87 % overall accuracy in KL grading, with enhanced sensitivity to subtle changes in early-stage KOA (Grades 1–2).