Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Automatic detection of joints and quantification of knee osteoarthritis severity using convolutional neural networks

Antony, Joseph orcid logoORCID: 0000-0001-6493-7829, McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477, Moran, Kieran orcid logoORCID: 0000-0003-2015-8967 and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2017) Automatic detection of joints and quantification of knee osteoarthritis severity using convolutional neural networks. In: 13th International Conference on Machine Learning and Data Mining, 15-20 July 2017, New York, USA.. ISBN 978-3-319-62416-7

Abstract
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.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Knee Osteoarthritis; KL grades; Automatic Detection; Fully Convolutional Neural Networks; Classification; Regression
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
DCU Faculties and Schools > Faculty of Science and Health > School of Health and Human Performance
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: 13th International Conference on Machine Learning and Data Mining, Proceedings. Lecture Notes in Computer Science 10358. Springer International Publishing. ISBN 978-3-319-62416-7
Publisher:Springer International Publishing
Official URL:https://dx.doi.org/10.1007/978-3-319-62416-7_27
Copyright Information:© 2017 Springer. The original publication is available at www.springerlink.com
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:21761
Deposited On:17 Jul 2017 08:42 by Joseph Antony . Last Modified 12 Aug 2020 13:21
Documents

Full text available as:

[thumbnail of MLDM_CameraReady.pdf]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
1MB
Downloads

Downloads

Downloads per month over past year

Archive Staff Only: edit this record