Computer aided diagnosis of Alzheimer's disease using component based SVM

Authors:
I. A. Illán;J. M. Górriz;M. M. López;J. Ramírez;D. Salas-Gonzalez;F. Segovia;R. Chaves;C. G. Puntonet
Affiliations:
Dept. of Signal Theory, Networking and Communications, 18071 University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, 18071 University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, 18071 University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, 18071 University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, 18071 University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, 18071 University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, 18071 University of Granada, Spain;Dept. of Computers Architecture and Technology, 18071 University of Granada, Spain
Venue:
Applied Soft Computing
Year:
2011

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Abstract

Abstract: Alzheimer's disease (AD) is a progressive neurodegenerative disorder first affecting memory functions and then gradually affecting all cognitive functions with behavioural impairments and eventually causing death. Functional brain imaging as single-photon emission computed tomography (SPECT) is commonly used to guide the clinician's diagnosis. However, conventional evaluation of these scans often relies on manual reorientation, visual reading and semi-quantitative analysis of certain regions of the brain. These steps are time consuming, subjective and prone to error. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the AD. The proposed approach is based on a first automatic feature selection, and secondly a combination of component-based support vector machine (SVM) classification and a pasting votes technique of assembling SVM classifiers.