Alzheimer's Diagnosis Using Eigenbrains and Support Vector Machines

Authors:
I. Álvarez;J. M. Górriz;J. Ramírez;D. Salas-Gonzalez;M. López;F. Segovia;C. G. Puntonet;B. Prieto
Affiliations:
Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Computers Architecture and Technology, University of Granada, Spain;Dept. of Computers Architecture and Technology, University of Granada, Spain
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

Citing 6
Cited 1

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Abstract

An accurate and early diagnosis of the Alzheimer's Disease (AD) is of fundamental importance for the patients medical treatment. Single Photon Emission Computed Tomography (SPECT) images are commonly used by physicians to assist the diagnosis, rating them by visual evaluations. In this work we present a computer assisted diagnosis tool based on a Principal Component Analysis (PCA) dimensional reduction of the feature space approach and a Support Vector Machine (SVM) classification method for improving the AD diagnosis accuracy by means of SPECT images. The most relevant image features were selected under a PCA compression, which diagonalizes the covariance matrix, and the extracted information was used to train a SVM classifier which could classify new subjects in an unsupervised manner.