Paper Info
Title
Comparison And Analyzation Of Different Feature Parameters For Alzheimer'S Disease Identification
Abstract
In this paper, we compare the performance of the derived anatomical features and the extracted feature parameters in Alzheimer's disease (AD) identification. The correlation relationship between them and clinical mini-mental state examination (MMSE) score is analyzed. Based on these feature parameters, the highly correlated combined feature vectors are built and used as variables for the presented modified elastic net (EN) classifier. Experimental results show that the extracted feature parameters can obtain similar identification performance with the cortical thickness and the volume of gray matter (GM) in AD identification. The highly correlated combined feature vectors show the best identification performance among all of feature parameters using the modified EN-based classifier.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8856358
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Correlation coefficient,Computer vision,Feature vector,Pattern recognition,Elastic net regularization,Computer science,Feature extraction,Correlation,Artificial intelligence,Classifier (linguistics),Principal component analysis
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
1
0.35
References 
Authors
0
6
Name
Order
Citations
PageRank
Yan Liu1213.88
Xiangzhu Zeng2134.24
Ling Wang311.36
Hong Cheng470365.27
Zheng Wang510.35
Qiang Wang613.05