NDLI: Comparison of artificial neural network and Bayesian belief network in a computer-assisted diagnosis scheme for mammography

Content Provider	IEEE Xplore Digital Library
Author	Bin Zheng Yuan-Hsiang Chang Xiao-Hui Wang Good, W.F.
Copyright Year	1999
Description	Author affiliation: Dept. of Radiol., Pittsburgh Univ., PA, USA (Bin Zheng)
Abstract	Artificial neural networks (ANN) have been widely used in computer-assisted diagnosis (CAD) schemes as a classification tool to identify abnormalities in digitized mammograms. Because of certain limitations of ANNs, some investigators argue that Bayesian belief network (BBN) may exhibit higher performance. In this study we compared the performance of an ANN and a BBN used in the same CAD scheme. The common databases and the same genetic algorithm (GA) were used to optimize both networks. The experimental results demonstrated that using GA optimization, the performance of the two networks converged to the same level in detecting masses from digitized mammograms. Therefore, in this study we concluded that improving the performance of CAD schemes might be more dependent on optimization of feature selection and diversity of training database than on any particular machine classification paradigm.
Starting Page	4181
Ending Page	4185
File Size	549058
Page Count	5
File Format	PDF
ISBN	0780355296
ISSN	10987576
DOI	10.1109/IJCNN.1999.830835
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-07-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Bayesian methods Intelligent networks Computer aided diagnosis Mammography Feature extraction Machine learning Genetic algorithms Pattern recognition Humans
Content Type	Text
Resource Type	Article

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