Automatic Diagnosis of Knee Osteoarthritis Severity Using Swin Transformers

Knee osteoarthritis is a widespread condition that can cause chronic pain and loss of mobility. Early detection and grading are critical for effective intervention, yet manual assessment remains labour intensive.

We propose an automated framework that employs a Swin Transformer backbone with a multi-head prediction module to capture both global context and fine-grained biomarkers in radiographic images. The model leverages publicly available datasets labelled with Kellgren–Lawrence scores and incorporates a domain adaptation strategy to mitigate dataset shift. Experiments show that the approach delivers reliable severity predictions and generalises across data sources.