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Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks

Antony, Joseph, McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477, O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 and Moran, Kieran orcid logoORCID: 0000-0003-2015-8967 (2016) Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks. In: 23rd International Conference on Pattern Recognition, 4-8 Dec. 2016, Cancun, Mexico.

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.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Knee osteoarthritis;KL grades; Convolutional neural network; classification; regression; wndchrm.
Subjects:Computer Science > Machine learning
Computer Science > Artificial intelligence
Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Science and Health > School of Health and Human Performance
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:21355
Deposited On:08 Dec 2016 11:19 by Joseph Antony . Last Modified 12 Aug 2020 13:21
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