Classification of MR and CT images using genetic algorithms

Abstract

A modified restricted Coulomb energy (MoRCE) network trained by the genetic algorithm is presented. Each neuron of the network forms a closed region in the input space. The closed regions which are formed by the neurons overlap each other, like STAR. Genetic algorithms are used to improve the classification performances of the magnetic resonance (MR) and computer tomography (CT) images with minimized number of neurons. MoRCE is examined comparatively with multilayer perceptron (MLP), and restricted Coulomb energy (RCE). It is observed that MoRCE gives the best classification performance with less number of neurons after a short training time.