TY - JOUR
T1 - Voxel-Based Morphometry
T2 - Improving the Diagnosis of Alzheimer's Disease Based on an Extreme Learning Machine Method from the ADNI cohort
AU - Zhang, Feng
AU - Tian, Sijia
AU - Chen, Sipeng
AU - Ma, Yuan
AU - Li, Xia
AU - Guo, Xiuhua
N1 - Publisher Copyright:
© 2019
PY - 2019/8/21
Y1 - 2019/8/21
N2 - Computer-aided diagnosis has become a widely-used auxiliary tool for the diagnosis of Alzheimer's disease (AD). In this study, we developed an extreme learning machine (ELM) model to discriminate between patients with AD and normal controls (NCs) using voxel-based morphometry (VBM) obtained from magnetic resonance imaging. Support vector machine (SVM), Gaussian process regression (GPR), and partial least squares (PLS) regression were compared with the ELM model. The calculated characteristics, i.e., texture features, VBM parameters, and clinical information, were adopted as the classification features. A 10-fold cross validation was used to evaluate the performance of ELM, SVM, GPR, and PLS models. We applied the proposed methods to data from 58 patients with AD and 94 NCs, and achieved a classification accuracy of up to 0.96 with all classification features of the ELM model, while the results of the other three models were 0.82 (PLS), 0.79 (GPR), and 0.75 (SVM). Furthermore, the effect of VBM parameter modeling is better than texture parameter. Thus, our method was optimal in distinguishing patients with AD from NCs, and may therefore be useful for the diagnosis of AD.
AB - Computer-aided diagnosis has become a widely-used auxiliary tool for the diagnosis of Alzheimer's disease (AD). In this study, we developed an extreme learning machine (ELM) model to discriminate between patients with AD and normal controls (NCs) using voxel-based morphometry (VBM) obtained from magnetic resonance imaging. Support vector machine (SVM), Gaussian process regression (GPR), and partial least squares (PLS) regression were compared with the ELM model. The calculated characteristics, i.e., texture features, VBM parameters, and clinical information, were adopted as the classification features. A 10-fold cross validation was used to evaluate the performance of ELM, SVM, GPR, and PLS models. We applied the proposed methods to data from 58 patients with AD and 94 NCs, and achieved a classification accuracy of up to 0.96 with all classification features of the ELM model, while the results of the other three models were 0.82 (PLS), 0.79 (GPR), and 0.75 (SVM). Furthermore, the effect of VBM parameter modeling is better than texture parameter. Thus, our method was optimal in distinguishing patients with AD from NCs, and may therefore be useful for the diagnosis of AD.
KW - Alzheimer's disease
KW - extreme learning machine
KW - magnetic resonance imaging
KW - voxel-based morphometry
UR - http://www.scopus.com/inward/record.url?scp=85069686586&partnerID=8YFLogxK
U2 - 10.1016/j.neuroscience.2019.05.014
DO - 10.1016/j.neuroscience.2019.05.014
M3 - Article
C2 - 31102761
AN - SCOPUS:85069686586
SN - 0306-4522
VL - 414
SP - 273
EP - 279
JO - Neuroscience
JF - Neuroscience
ER -