Study on factors affecting classification of Alzheimer's disease based on structural MRI
LI Jianzhong
ZENG An
PAN Dan
SONG Xiaowei
GUO Hui
WANG Zhuowei
Abstract：Aiming at the problem of classifying Alzheimer＇s disease (AD) and its prodromal stage,the factors such as training sample selection, feature extraction and classification algorithm selection were studied to exhibit their importance in improving the clas-sification accuracy.Support vector machine (SVM) modeling method based on three types of anatomical features was proposed.Three types of anatomical features (the volume of gray matter, the surface area and the average thickness of the cerebral cortex) in different brain regions were extracted by 3D reconstruction of sMRI images and were utilized to build a SVM model.With the help of 10-fold cross validation, the trained SVM model was employed to classify AD patients,patients with mild cognitive impairment (MCI) and healthy subjects (HC).Compared with other classification results based on different data sets and different features published in other research papers, the experimental results in this study exhibite that it is more important to select the appropriate training data sets and features than to select the classification algorithm.This conclusion might be helpful for the further research on the computer-aided di-agnosis of AD.
Keywords：Alzheimer's diseaseMild cognitive impairmentStructural magnetic resonance imagingThree-dimensional recon-structionSupport vector machine
Publication Date：2018-01-01
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：5( 177-181 )