  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic
pain and stiffness in the knee joint. Early detection and diagnosis are crucial
for successful clinical intervention and management to prevent severe
complications, such as loss of mobility. In this paper, we propose an automated
approach that employs the Swin Transformer to predict the severity of KOA. Our
model uses publicly available radiographic datasets with Kellgren and Lawrence
scores to enable early detection and severity assessment. To improve the
accuracy of our model, we employ a multi-prediction head architecture that
utilizes multi-layer perceptron classifiers. Additionally, we introduce a novel
training approach that reduces the data drift between multiple datasets to
ensure the generalization ability of the model. The results of our experiments
demonstrate the effectiveness and feasibility of our approach in predicting KOA
severity accurately.
