Paper Info
Title
Assisting the Diagnosis of Neurodegenerative Disorders Using Principal Component Analysis and TensorFlow.
Abstract
Neuroimaging data provides a valuable tool to assist the diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) and Parkinson's disease (PD). During last years many research efforts have focused on the development of computer systems that automatically analyze neuroimaging data and allow improving the diagnosis of those diseases. This field has benefited from modern machine learning techniques, which provide a higher generalization ability, however the high dimensionality of the data is still a challenge and there is room for improvement. In this work we demonstrate a computer system based on Principal Component Analysis and TensorFlow, the machine learning library recently released by Google. The proposed system is able to successfully separate AD or PD patients from healthy subjects, as well as distinguishing between PD and other parkinsonian syndromes. The obtained results suggest that TensorFlow is a suitable environment to classify neuroimaging data and can help to improve the diagnosis of AD and Parkinsonism.
Year
DOI
Venue
2016
10.1007/978-3-319-47364-2_5
INTERNATIONAL JOINT CONFERENCE SOCO'16- CISIS'16-ICEUTE'16
Keywords
Field
DocType
Multivariate analysis,Machine learning,TensorFlow,Principal component analysis,Alzheimer's disease,Parkinson's disease
Parkinson's disease,Disease,Neuroscience,Parkinsonism,Parkinsonian syndromes,Neuroimaging,Multivariate analysis,Medicine,Principal component analysis
Conference
Volume
ISSN
Citations 
527
2194-5357
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Fermín Segovia17914.71
Marcelo García-Pérez200.68
J. M. Górriz357054.40
Javier Ramírez465668.23
Francisco Jesús Martínez-Murcia57417.08