Abstract

Info

Title:: Auxiliary diagnosis of Alzheimers disease based on feature extraction

Author(s):: LIU Xi; WANG Yu; FU Changyang; XIAO Hongbing; XING Suxia; School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China

Keywords:: Alzheimers disease magnetic resonance imaging support vector machine recursive feature elimination linear discriminant analysis

Abstract:: Alzheimers disease (AD) is a brain neuropathy which is common among the aged. The pathogenesis ofAD has not been known so far, and it is difficult to diagnose in the early stage of disease development.With the vigorous development of computers and artificial technologies, using magnetic resonance imaging (MRI) and machine learning methods to assist the diagnosis of AD has continuously made some new achievements. Herein a new method based on support vector machine-recursive feature elimination (SVM-RFE) and linear discriminant analysis (LDA) for the auxiliary diagnosis of AD is proposed. Firstly, the MRI images are preprocessed to obtain the gray matter volumes of 90 brain regions. Then the method combining SVM-RFE and LDA is used to select the significant features of the above gray matter volumes, and finally, the selected features are classified by SVM. By analyzing theMRI images of 34 patients withAD, 26 patients with subjective memory complaints (SMC) and 50 normal controls (NC) from ADNI database, the average classification accuracies of AD/NC, AD/SMC and NC/SMC reach 94.0%, 100.0% and 93.6%, respectively. The experimental results show that the proposed method can effectively extract features and assist doctors in the diagnosis ofAD.