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

Neha Bhooshan; Darrin Edwards; Maryellen Giger

doi:10.1117/12.878074

8 March 2011 Comparison of two-class and three-class Bayesian artificial neural networks in estimation of observations drawn from simulated bivariate normal distributions

Neha Bhooshan, Darrin Edwards, Maryellen Giger

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Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 796325 (2011) https://doi.org/10.1117/12.878074
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

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.

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Neha Bhooshan, Darrin Edwards, and Maryellen Giger "Comparison of two-class and three-class Bayesian artificial neural networks in estimation of observations drawn from simulated bivariate normal distributions", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796325 (8 March 2011); https://doi.org/10.1117/12.878074

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KEYWORDS

Computer simulations

Statistical analysis

Computer aided diagnosis and therapy

Artificial neural networks

Matrices

Binary data

Breast

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