A Hybrid Convolutional Neural Network Model Based on Different Evolution for Medical Image Classification

In clinical practice, the X-Ray image classification for the presence or absence of lesions by the medical auxiliary diagnosis system is the basic work, and the classification of lesion images and normal images can be completed by reading the medical X-Ray graphics. The actual process is the convolution and pooling layer of CNN to extract the features of medical images, and the full connection layer of CNN is used to classify the local information. The structure of CNN needs to be adjusted repeatedly. This paper focuses on utilizing Differential Evolution (DE) to automatically search for the optimal architecture of CNN. The research idea of this paper is to use the global optimization ability of Differential Evolution algorithm to regulate the structure of CNN. When the termination condition is satisfied, DE-CNN can automatically adjust the parameters and optimize the structure of the CNN. Among them, for mutation operation, we study a new mutation strategy in this paper, which inherits the vector that may solve the next generation, and appropriately accepts the basic elements of the inferior solution to increase the disturbance of the new species. The optimal individual is found through the optimization process of the DE algorithm, and the CNN model composed of all basic elements in the individual and trained on the medical X-Ray image data set is saved. The new mutation strategy generates mutation solution vector, through experimental verification which is beneficial to the optimization of CNN structure, and the accurate value of DE-CNN algorithm processing medical image classification is better than GoogLenet, ResNet34, VGGNet16, DenseNet, Lenet, AlexNet etc network structure.