Genetic Algorithm Optimization Of Convolutional Neural Network For Liver Cancer CT Image Classification

Abstract

In order to classify the liver CT images and make the initial weight of the convolution neural network to the optimal state. We propose a method of combine the genetic algorithm and convolution neural network to classify the liver CT tumor images which can be divided these liver CT images into two parts, have cancer and no cancer. This method used the global optimization and survival of fittest features of genetic algorithms and generated initial weight for convolution neural network by the operation of selection and crossover and mutation. After improvement, learning performance is better than traditional convolution neural network. According to simulation, the method of combining the genetic algorithm and convolution neural network has higher classification accuracy than traditional convolution neural network and support vector machine. It can help medical-aided diagnosis better.