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A comparative study of existing and new deep learning methods for detecting knee injuries using the MRNet dataset

Azcona, David orcid logoORCID: 0000-0003-3693-7906, McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477 and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2020) A comparative study of existing and new deep learning methods for detecting knee injuries using the MRNet dataset. In: 2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA), 19-22 Oct 2020, Valencia, Spain (Online).

Abstract
This work presents a comparative study of existing and new techniques to detect knee injuries by leveraging Stanford's MRNet Dataset. All approaches are based on deep learning and we explore the comparative performances of transfer learning and a deep residual network trained from scratch. We also exploit some characteristics of Magnetic Resonance Imaging (MRI) data by, for example, using a fixed number of slices or 2D images from each of the axial, coronal and sagittal planes as well as combining the three planes into one multi-plane network. Overall we achieved a performance of 93.4% AUC on the validation data by using the more recent deep learning architectures and data augmentation strategies. More flexible architectures are also proposed that might help with the development and training of models that process MRIs. We found that transfer learning and a carefully tuned data augmentation strategy were the crucial factors in determining best performance.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:knee injury; ACL tear; Magnetic Resonance Imaging; MRI; Deep Learning; ACL
Subjects:Computer Science > Artificial intelligence
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Publisher:Institute of Electrical and Electronics Engineers (IEEE)
Official URL:https://dx.doi.org/10.1109/IDSTA50958.2020.9264030
Copyright Information:© IEEE
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland grant SFI/12/RC/2289 P2 and SFI/15/SIRG/3283
ID Code:25068
Deposited On:23 Oct 2020 11:17 by Alan Smeaton . Last Modified 13 May 2021 14:15
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