Particle swarm optimization of neural network CAD systems with clinically relevant objectives

Abstract

Neural networks (NN) are typically developed to minimize the squared difference between the network's output and the target value for a set of training patterns; namely the mean squared error (MSE). However, lower MSE does not necessarily translate into a clinically more useful decision model. The purpose of this study was to investigate the particle swarm optimization (PSO) algorithm as an alternative way of NN optimization with clinically relevant objective functions (e.g., ROC and partial ROC area indices). The PSO algorithm was evaluated with respect to a NN-based CAD system developed to discriminate mammographic regions of interest (ROIs) that contained masses from normal regions based on 8 computer-extracted morphology-oriented features. Neural networks were represented as points (particle locations) in a D-dimensional search/optimization space where each dimension corresponded to one adaptable NN parameter. The study database of 1,337 ROIs (681 with masses, 656 normal) was split into two subsets to implement two-fold cross-validation sampling scheme. Neural networks were optimized with the PSO algorithm and the following objective functions (1) MSE, (2) ROC area index AUC, and (3) partial ROC area indices TPFAUC with TPF=0.90 and TPF=0.98. For comparison, performance of neural networks of the same architecture trained with the traditional backpropagation algorithm was also evaluated. Overall, the study showed that when the PSO algorithm optimized network parameters using a particular training objective, the NN test performance was superior with respect to the corresponding performance index. This was particularly true for the partial ROC area indices where statistically significant improvements were observed.