Pneumonia image classification method based on improved convolutional neural network

Abstract

This is an exploration of the recognition technology of pneumonia pictures based on convolutional neural network technology. Among them, the recognition model used is based on the study of hundreds of real X-rays of lung pictures database, which contains not only lung pictures of real pneumonia patients, but also lung pictures of normal people. This article describes the most popular techniques of the moment, convolutional neural networks, which are widely used in areas such as image recognition or machine learning and are recognized by most people. This paper analyzes the specific implementation techniques of convolutional neural networks used, and uses some new methods to optimize and implement this algorithm, so as to achieve a better model structure and accuracy. Among them, with regard to the pooling layer, working between the convolutional layer and the final output layer, this paper compares various pooling methods and finally yields the maximum pooled neural network is more stable, and the average pooled neural network is more effective for large databases. The final use, pooling the resulting model accuracy is about 95% by maximum pooled neural network.