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Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networks

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
This paper proposes a new approach to automatically quantify the severity of knee osteoarthritis (OA) from radiographs using deep convolutional neural networks (CNN). Clinically, knee OA severity is assessed using Kellgren & Lawrence (KL) grades, a five point scale. Previous work on automatically predicting KL grades from radiograph images were based on training shallow classifiers using a variety of hand engineered features. We demonstrate that classification accuracy can be significantly improved using deep convolutional neural network models pre-trained on ImageNet and fine-tuned on knee OA images. Furthermore, we argue that it is more appropriate to assess the accuracy of automatic knee OA severity predictions using a continuous distance-based evaluation metric like mean squared error than it is to use classification accuracy. This leads to the formulation of the prediction of KL grades as a regression problem and further improves accuracy. Results on a dataset of X-ray images and KL grades from the Osteoarthritis Initiative (OAI) show a sizable improvement over the current state-of-the-art.
Conference Name: 
23rd International Conference on Pattern Recognition
Proceedings: 
Proceedings of 23rd International Conference on Pattern Recognition
Digital Object Identifer (DOI): 
10.na
Publication Date: 
05/12/2016
Conference Location: 
Mexico
Institution: 
Dublin City University (DCU)
Open access repository: 
Yes