Optimal selection of neural network architecture for CAD using simulated annealing

Many computer-aided diagnosis (CAD) systems use neural networks for either detection or classification of abnormalities on medical images. In this work, the authors investigate an automated technique to optimally select the neural network architecture using the simulated annealing algorithm. The optimization is based on the area A/sub z/ under the receiver operating characteristic (ROC) curve of the neural network. Studies are performed to select the architecture of a convolution neural network designed for the classification of true and false microcalcifications detected on digitized mammograms.