Classification of Knee Osteoarthritis Severity by Transfer Learning From X-Ray Images

Abstract

Knee Osteoarthritis (KOA) is the most common type of arthritis and its severity is assessed with the Kellgren-Lawrence (KL) grading system based on evidence from both knee bones. Recent advancements point to an era where computer-assisted methods enhance KOA diagnostic efficiency. This study implemented binary and multiple classification processes based on X-ray images and deep learning algorithms for computer-aided KOA severity diagnosis. Pre-processing involved extracting the region of interest and contrast enhancement with CLAHE on the X-ray images from the included dataset. Using this dataset, 2, 3, 4, and 5 class classification processes were conducted with ResNet-50, Xception, VGG16, EfficientNetb0, and DenseNet201 transfer learning models. Each model was assessed with “rmsprop,” “sgdm,” and “adam” optimization algorithms. Study findings reveal that, the DenseNet201-rmsprop model achieved 87.7% accuracy, 87.2% F1-Score, and a 0.75 Cohen’s kappa value for 2-class classification. For 3-class classification, it achieved 85.6% accuracy, 82.4% F1-Score, and a 0.71 Cohen’s kappa value. For 4-class classification, the DenseNet201-rmsprop model provided 81.5% accuracy, 77.1% F1-Score, and a Cohen’s kappa value of 0.67. In the 5-class classification, the highest success was with the Xception-rmsprop model, with 67.8% accuracy, 68.8% F1-Score, and a 0.55 Cohen’s kappa value. The evaluation with varying class numbers and different transfer learning models highlights the proposed approach’s effectiveness. Results of the study underscore the study’s uniqueness and success in demonstrating how varying the number of classes, employing different transfer learning models and optimizers can provide clearer insights into KOA severity evaluation.