# Automatic diagnosis of knee osteoarthritis severity using Swin transformer

arXiv: https://arxiv.org/abs/2307.04442 · HTML: https://mlsanity.com/paper/2307.04442/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 10 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach](https://mlsanity.com/paper/1710.10589/) (2017)
- [Training Vision Transformers with Only 2040 Images](https://mlsanity.com/paper/2201.10728/) (2022)
- [Automatic Grading of Individual Knee Osteoarthritis Features in Plain Radiographs using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1907.08020/) (2019)
- [Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1609.02469/) (2016)
- [Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks](https://mlsanity.com/paper/1703.09856/) (2017)
- [Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists](https://mlsanity.com/paper/2207.12521/) (2022)
- [Deep-based quality assessment of medical images through domain adaptation](https://mlsanity.com/paper/2210.10533/) (2022)

## Built on by (most cited first, 2 of 2)

- [Shifting Focus: From Global Semantics to Local Prominent Features in Swin-Transformer for Knee Osteoarthritis Severity Assessment](https://mlsanity.com/paper/2403.09947/) (2024)
- [Morphology-Aware KOA Classification: Integrating Graph Priors with Vision Models](https://mlsanity.com/paper/2510.21801/) (2025)

## Abstract (arXiv)

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.