Format

Send to

Choose Destination
Neurosci Lett. 2009 Oct 30;464(3):233-8. doi: 10.1016/j.neulet.2009.08.061. Epub 2009 Aug 28.

SVM-based CAD system for early detection of the Alzheimer's disease using kernel PCA and LDA.

Author information

1
Dept. of Signal Theory, Networking and Communications, University of Granada, Spain. miriamlp@ugr.es

Abstract

Single-photon emission tomography (SPECT) imaging has been widely used to guide clinicians in the early Alzheimer's disease (AD) diagnosis challenge. However, AD detection still relies on subjective steps carried out by clinicians, which entail in some way subjectivity to the final diagnosis. In this work, kernel principal component analysis (PCA) and linear discriminant analysis (LDA) are applied on functional images as dimension reduction and feature extraction techniques, which are subsequently used to train a supervised support vector machine (SVM) classifier. The complete methodology provides a kernel-based computer-aided diagnosis (CAD) system capable to distinguish AD from normal subjects with 92.31% accuracy rate for a SPECT database consisting of 91 patients. The proposed methodology outperforms voxels-as-features (VAF) that was considered as baseline approach, which yields 80.22% for the same SPECT database.

PMID:
19716856
DOI:
10.1016/j.neulet.2009.08.061
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center