Kollias, Stefanos, Bidaut, Luc, Wingate, James and Kollia, Ilianna (2020) A Unified Deep Learning Approach for Prediction of Parkinson’s Disease. IET Image Processing, 14 (10). pp. 1980-1989. ISSN 1751-9659
Full content URL: https://doi.org/10.1049/iet-ipr.2019.1526
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| Item Type: | Article |
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| Item Status: | Live Archive |
Abstract
The paper presents a novel approach, based on deep learning, for diagnosis of Parkinson’s disease through medical
imaging. The approach includes analysis and use of the knowledge extracted by Deep Convolutional and Recurrent Neural Networks (DNNs) when trained with medical images, such as Magnetic Resonance Images and DaTscans. Internal representations of the trained DNNs constitute the extracted knowledge which is used in a transfer learning and domain adaptation manner, so as to create a unified framework for prediction of Parkinson’s across different medical environments. A large experimental study is presented illustrating the ability of the proposed approach to effectively predict Parkinson’s, using different medical image sets from real environments.
| Keywords: | deep learning, transfer learning, domain adaptation, unsupervised feature clustering, medical prediction, medical imaging, Parkinson's |
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| Subjects: | G Mathematical and Computer Sciences > G700 Artificial Intelligence G Mathematical and Computer Sciences > G400 Computer Science G Mathematical and Computer Sciences > G730 Neural Computing G Mathematical and Computer Sciences > G740 Computer Vision G Mathematical and Computer Sciences > G760 Machine Learning |
| Divisions: | College of Science > School of Computer Science |
| ID Code: | 41398 |
| Deposited On: | 07 Jul 2020 11:35 |
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