Pneumonia Recognition by Deep Learning: A Comparative Investigation

Abstract

Pneumonia is a common infectious disease. Currently, the most common method of pneumonia identification is manual diagnosis by professional doctors, but the accuracy and identification efficiency of this method is not satisfactory, and computer-aided diagnosis technology has emerged. With the development of artificial intelligence, deep learning has also been applied to pneumonia diagnosis and can achieve high accuracy. In this paper, we compare five deep learning models in different situations for pneumonia recognition. The objective was to employ five deep learning models to identify pneumonia X-ray images and to compare and analyze them in different cases, thus screening out the optimal model for each type of case to improve the efficiency of pneumonia recognition and further apply it to the computer-aided diagnosis of pneumonia species. In the proposed framework: (1) datasets are collected and processed, (2) five deep learning models for pneumonia recognition are built, (3) the five models are compared, and the optimal model for each case is selected. The results show that the LeNet5 and AlexNet models achieved better pneumonia recognition for small datasets, while the MobileNet and ResNet18 models were more suitable for pneumonia recognition for large datasets. The comparative analysis of each model under different situations can provide a deeper understanding of the efficiency of each model in identifying pneumonia, thus making the practical application and selection of deep learning models for pneumonia recognition more convenient.