Open AccessUnified deep learning approach for prediction of Parkinson's disease
Author(s) -
Wingate James,
Kollia Ilianna,
Bidaut Luc,
Kollias Stefanos
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.1526
Subject(s) - deep learning , artificial intelligence , computer science , convolutional neural network , domain adaptation , transfer of learning , machine learning , parkinson's disease , domain (mathematical analysis) , medical imaging , artificial neural network , magnetic resonance imaging , adaptation (eye) , pattern recognition (psychology) , disease , neuroscience , medicine , psychology , mathematics , pathology , mathematical analysis , classifier (uml) , radiology
The study 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 when trained with medical images, such as magnetic resonance images and dopamine transporters scans. 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.