Brain image recognition algorithm and high performance computing of internet of medical things based on convolutional neural network

Abstract

Due to the wide variety of medical images and the complexity of the human body structure, the characteristics of manual extraction of medical images are difficult, the adaptive ability is poor, and the classification effect needs to be improved. Aiming at the shortcomings of traditional medical image recognition methods, this paper proposes an adaptive convolutional neural network model CNN-BN-PReLU based on the convolutional neural network method. The model first performs batch normalization (BN) processing on the input of each feature map of each layer of network, and then adaptively adjusts the parameters by using Parametric Rectified Linear Unit (PReLU) to compare the BN algorithm. Based on the performance before and after the activation function, an adaptive convolutional neural network model is constructed. The experimental results show that the model can abstract the image features without artificial intervention, speed up the network convergence speed and shorten the training time, and significantly improve the image recognition rate and reduce the misdiagnosis rate and missed diagnosis rate of the disease.