Paper Info
Title
Computer-aided diagnosis of Alzheimer's disease using multiple features with artificial neural network
Abstract
Alzheimer's disease (AD) is a progressively neuro-degenerative disorder. In the AD-related research, the volumetric analysis of hippocampus is the most extensive study. However, the segmentation and identification of the hippocampus are highly complicated and time-consuming. Therefore, a MRI-based classification framework is proposed to differentiate between AD's patients and normal individuals. First, volumetric features and shape features were extracted from MRI data. Afterward, Principle component analysis (PCA) was utilized to decrease the dimensions of feature space. Finally, a Back-propagation artificial neural network (ANN) classifier was trained for AD classification. With the proposed framework, the classification accuracy is reached to 88.27% by only using volumetric features and shape features. And, the result achieved up to 92.17% by using volumetric features and shape features with the PCA.
Year
DOI
Venue
2010
10.1007/978-3-642-15246-7_72
PRICAI
Keywords
Field
DocType
shape feature,back-propagation artificial neural network,proposed framework,mri-based classification framework,classification accuracy,volumetric feature,principle component analysis,multiple feature,ad-related research,computer-aided diagnosis,ad classification,volumetric analysis,feature space,artificial neural network,magnetic resonance imaging,back propagation,magnetic resonance image
Feature vector,Pattern recognition,Segmentation,Computer science,Computer-aided diagnosis,Artificial intelligence,Classifier (linguistics),Artificial neural network,Machine learning,Principal component analysis
Conference
Volume
ISSN
ISBN
6230
0302-9743
3-642-15245-7
Citations 
PageRank 
References 
2
0.41
3
Authors
6
Name
Order
Citations
PageRank
Shih-Ting Yang1254.46
Jiann-Der Lee221134.02
Chung-Hsien Huang3397.92
Jiun-Jie Wang4223.67
Wen-Chuin Hsu571.19
Yau-Yau Wai6102.12