3D-DWT Improves Prediction of AD and MCI

DOI

10.2991/iset-15.2015.16How to use a DOI?

Keywords

Discrete wavelet transform, Magnetic resonance imaging, Multiclass kernel support vector machine, Cross validation, Alzheimer's disease, Mild cognitive impairment

Abstract

In order to predict subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI) from normal elder controls (NC) more accurately, we compared two different kinds of discrete wavelet transform (DWT) based feature extraction techniques: multi-slice 2D-DWT and 3D-DWT. The dataset contained the magnetic resonance (MR) images of 178 subjects consisting of 97 NCs, 57 MCIs, and 24 ADs. We constructed two multiclass kernel support vector machine (MKSVM) classifiers based on multislice 2D-DWT features and 3D-DWT features, respectively. 5-fold cross validation was employed to obtain the out-of-sample estimate. Each classifier runs 10 times. Welch’s t-test showed that the mean of the overall accuracy by 3D-DWT was higher than that of multislice 2D-DWT, and the difference was statistically significant (p=0.0146).

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shuihua Wang
AU  - Yi Chen
AU  - Yudong Zhang
AU  - Zhengchao Dong
AU  - Elizabeth Lee
AU  - Preetha Phillips
PY  - 2015/03
DA  - 2015/03
TI  - 3D-DWT Improves Prediction of AD and MCI
BT  - Proceedings of the First International Conference on Information Science and Electronic Technology
PB  - Atlantis Press
SP  - 60
EP  - 63
SN  - 2352-538X
UR  - https://doi.org/10.2991/iset-15.2015.16
DO  - 10.2991/iset-15.2015.16
ID  - Wang2015/03
ER  -