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Proceedings Paper

Comparison of two-class and three-class Bayesian artificial neural networks in estimation of observations drawn from simulated bivariate normal distributions
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Paper Abstract

The development and application of multi-class BANN classifiers in computer-aided diagnosis methods motivated this study in which we compared estimates produced by two-class and three-class BANN classifiers to true observations drawn from simulated distributions. Observations were drawn from three Gaussian bivariate distributions with distinct means and variances to generate G1, G2, and G3 simulated datasets. A two-class BANN was trained on each training dataset for a total of ten different trained BANNs. The same testing dataset was run on each trained BANN. The average and standard deviation of the resulting ten sets of BANN outputs were then calculated. This process was repeated with three-class BANNS. Different sample numbers and values of a priori probabilities were investigated. The relationship between the average BANN output and true distribution was measured using Pearson and Spearman coefficients, R-squared and mean square error for two-class and three-class BANNs. There was significantly high correlation between the average BANN output and true distribution for two-class and three-class BANNs; however, subtle non-linearities and spread were found in comparing the true and estimated distributions. The standard deviations of two-class and three-class BANNs were comparable, demonstrating that three-class BANNs can perform as reliably as two-class BANN classifiers in estimating true distributions and that the observed non-linearities and spread were not simply due to statistical uncertainty but were valid characteristics of the BANN classifiers. In summary, three-class BANN decision variables were similar in performance to those of two-class BANNs in estimating true observations drawn from simulated bivariate normal distributions.

Paper Details

Date Published: 9 March 2011
PDF: 8 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796325 (9 March 2011); doi: 10.1117/12.878074
Show Author Affiliations
Neha Bhooshan, The Univ. of Chicago (United States)
Darrin Edwards, The Univ. of Chicago (United States)
Maryellen Giger, The Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers; Bram van Ginneken, Editor(s)

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