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

Piotr A. Habas; Jacek M. Zurada; Adel S. Elmaghraby; Georgia D. Tourassi

doi:10.1117/12.713829

29 March 2007 Particle swarm optimization of neural network CAD systems with clinically relevant objectives

Piotr A. Habas, Jacek M. Zurada, Adel S. Elmaghraby, Georgia D. Tourassi

Proceedings Volume 6514, Medical Imaging 2007: Computer-Aided Diagnosis; 65140M (2007) https://doi.org/10.1117/12.713829
Event: Medical Imaging, 2007, San Diego, CA, United States

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.

Citation Download Citation

Piotr A. Habas, Jacek M. Zurada, Adel S. Elmaghraby, and Georgia D. Tourassi "Particle swarm optimization of neural network CAD systems with clinically relevant objectives", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65140M (29 March 2007); https://doi.org/10.1117/12.713829

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available