SVM-based computer-aided diagnosis of the Alzheimer's disease using t-test NMSE feature selection with feature correlation weighting

被引：112

作者：

Chaves, R. ^{[1
]}

Ramirez, J. ^{[1
]}

Gorriz, J. M. ^{[1
]}

Lopez, M. ^{[1
]}

Salas-Gonzalez, D. ^{[1
]}

Alvarez, I. ^{[1
]}

Segovia, F. ^{[1
]}

机构：

[1] Univ Granada, Dept Signal Theory Networking & Commun, E-18071 Granada, Spain

来源：

NEUROSCIENCE LETTERS | 2009年 / 461卷 / 03期

关键词：

SPECT Brain Imaging Classification; Computer-aided diagnosis; Alzheimer's disease; Support Vector machine; SPECT; CLASSIFICATION;

D O I：

10.1016/j.neulet.2009.06.052

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

This letter shows a computer-aided diagnosis (CAD) technique for the early detection of the Alzheimer's disease (AD) based on single photon emission computed tomography (SPECT) image feature selection and a statistical learning theory classifier. The challenge of the curse of dimensionality is addressed by reducing the large dimensionality of the input data and defining normalized mean squared error features over regions of interest (ROI) that are selected by a t-test feature selection with feature correlation weighting. Thus, normalized mean square error (NMSE) features of cubic blocks located in the temporoparietal brain region yields peak accuracy values of 98.3% for almost linear kernel support vector machine (SVM) defined over the 20 most discriminative features extracted. This new method outperformed recent developed methods for early AD diagnosis. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

引用

页码：293 / 297

页数：5

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