# Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1703.09856 · HTML: https://mlsanity.com/paper/1703.09856/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2017.

## Built on by (most cited first, 16 of 16)

- [The Intrinsic Manifolds of Radiological Images and their Role in Deep Learning](https://mlsanity.com/paper/2207.02797/) (2022)
- [Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Network](https://mlsanity.com/paper/2106.14292/) (2021)
- [Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists](https://mlsanity.com/paper/2207.12521/) (2022)
- [Transformer with Selective Shuffled Position Embedding and Key-Patch Exchange Strategy for Early Detection of Knee Osteoarthritis](https://mlsanity.com/paper/2304.08364/) (2023)
- [Give me a knee radiograph, I will tell you where the knee joint area is: a deep convolutional neural network adventure](https://mlsanity.com/paper/2202.05382/) (2022)
- [Confidence-Driven Deep Learning Framework for Early Detection of Knee Osteoarthritis](https://mlsanity.com/paper/2303.13203/) (2023)
- [Automatic diagnosis of knee osteoarthritis severity using Swin transformer](https://mlsanity.com/paper/2307.04442/) (2023)
- [A Neural Template Matching Method to Detect Knee Joint Areas](https://mlsanity.com/paper/2209.11791/) (2022)
- [Shifting Focus: From Global Semantics to Local Prominent Features in Swin-Transformer for Knee Osteoarthritis Severity Assessment](https://mlsanity.com/paper/2403.09947/) (2024)
- [Coherence Learning using Keypoint-based Pooling Network for Accurately Assessing Radiographic Knee Osteoarthritis](https://mlsanity.com/paper/2112.09177/) (2021)
- [Knee arthritis severity measurement using deep learning: a publicly available algorithm with a multi-institutional validation showing radiologist-level performance](https://mlsanity.com/paper/2203.08914/) (2022)
- [Key-Exchange Convolutional Auto-Encoder for Data Augmentation in Early Knee Osteoarthritis Detection](https://mlsanity.com/paper/2302.13336/) (2023)
- [An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data](https://mlsanity.com/paper/2407.11500/) (2024)
- [Identity-Consistent Diffusion Network for Grading Knee Osteoarthritis Progression in Radiographic Imaging](https://mlsanity.com/paper/2407.21381/) (2024)
- [Diffusion-based Counterfactual Augmentation: Towards Robust and Interpretable Knee Osteoarthritis Grading](https://mlsanity.com/paper/2506.15748/) (2025)
- [Morphology-Aware KOA Classification: Integrating Graph Priors with Vision Models](https://mlsanity.com/paper/2510.21801/) (2025)

## Abstract (arXiv)

This paper introduces a new approach to automatically quantify the severity of knee OA using X-ray images. Automatically quantifying knee OA severity involves two steps: first, automatically localizing the knee joints; next, classifying the localized knee joint images. We introduce a new approach to automatically detect the knee joints using a fully convolutional neural network (FCN). We train convolutional neural networks (CNN) from scratch to automatically quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross-entropy and mean-squared loss. This joint training further improves the overall quantification of knee OA severity, with the added benefit of naturally producing simultaneous multi-class classification and regression outputs. Two public datasets are used to evaluate our approach, the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST), with extremely promising results that outperform existing approaches.