Year: 2015
Type: conference-paper
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).
Authors: Shuihua Wang, Yi Chen, Yudong Zhang, Zhengchao Dong, Elizabeth Lee, Preetha Phillips
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Awards: Mapping Abnormal Neurodevelopment in Schizophrenia, Functional-anatomic exploration of cognitive control, Morphometry Biomedical Informatics Research Network, G3213402248, G5441670805, G5932080728, Alzheimer's Disease Research Center, Healthy Aging and Senile Dementia, G7205106625, G7297363870, G7642477487, Neuromorphometry in Schizophrenia by Computer Algorithm, G9000239624, G9000300815
OpenAlex ID: W2009742110