Automatic Diagnosis of Knee Osteoarthritis Severity Using Swin Transformer

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

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