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

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.