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Author Tang-Kai Yin ♦ Nan-Tsing Chiu
Sponsorship IEEE ♦ IEEE Neural Networks Soc
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2005
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Brain ♦ Alzheimer's disease ♦ Support vector machines ♦ Support vector machine classification ♦ Positron emission tomography ♦ Biomedical imaging ♦ Medical diagnostic imaging ♦ Degenerative diseases ♦ Central nervous system ♦ Computed tomography
Abstract Alzheimer's disease is a chronic degenerative disease of the central nervous system. Most common regional abnormalities for Alzheimer's disease are symmetric or asymmetric bilateral temporal or parietal hypoperfusion. Single-photon emission computed tomography (SPECT) is a useful tool in analyzing hypoperfusion in patients with Alzheimer's disease. The aim of this research is to provide a quantitatively automatic analysis of the SPECT scans for the diagnosis of Alzheimer's disease. A characteristic-point-based fuzzy inference classifier (CPFIC) is proposed to perform two-class classification. The closeness matrix is defined to determine the closeness between training samples, and constrained minimizations are used to systematically train the CPFIC. For comparison, experiments on nearest neighbor method and support vector machine (SVM) were also performed. In error rates, the proposed CPFIC is better than nearest neighbor method, but worse than SVM method. Although the CPFIC did not perform better than SVM in error rates, the summarizing information embedded in the patterns on characteristic points can complement SVM to provide more information to radiologists
Description Author affiliation: Dept. of Comput. Sci. & Inf. Eng., Nat. Univ. of Kaohsiung (Tang-Kai Yin)
ISBN 0780391594
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-05-25
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 1.93 MB
Page Count 6
Starting Page 161
Ending Page 166

Source: IEEE Xplore Digital Library