Assisting the Diagnosis of Neurodegenerative Disorders Using Principal Component Analysis and TensorFlow
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Fermín Segovia [1] ; Marcelo García-Pérez [1] ; Juan Manuel Górriz [1] ; Javier Ramírez [1] ; Francisco Jesús Martínez-Murcia [1]
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[1] Universidad de Granada
Universidad de Granada
Granada, España
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- Localización: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings / coord. por Manuel Graña Romay
, José Manuel López Guede , Oier Etxaniz, Álvaro Herrero Cosío , Héctor Quintián Pardo , Emilio Santiago Corchado Rodríguez , 2017, ISBN 978-3-319-47364-2, págs. 43-52 - Idioma: inglés
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Resumen
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.
