Knee osteoarthritis severity grading using vision transformer

Abstract

Knee osteoarthritis severity grading from plain radiographs is of great significance in the diagnosis of osteoarthritis (OA). Recently, deep learning had a great impact on improving the Kellgren and Lawrence (KL) grading scheme of Knee osteoarthritis KOA using models that acquire the contextual features spontaneously without the need for any conventional high computational spatial configuration modeling. In this study, we apply the state-of-art Vision Transformer (ViT) for the KL grading of Knee Osteoarthritis and show that a simple transfer learning approach of such model can lead to better results than those achieved by other complex architectures over less number of training data. The study concludes that such a pre-trained ViT, fine-tuned on OAI dataset yield to promising results in KL grading KOA, in which these results are in line with the state-of-art studies.