Comparative Experiment of Convolutional Neural Network (CNN) Models Based on Pneumonia X-ray Images Detection

Abstract

This paper aims to reveal the relationship between the Convolutional Neural Network (CNN) model's behavior and the depth of the model. Due to the worldwide coronavirus pandemic, the training dataset is the chest x-ray images of the lungs, which are infected by pneumonia. The contrastive study incorporates three models: a classic model, which is the imitation of LeNet5, VGG16, and Residual Network 50. This research is based on pneumonia detection, and it can give people a deeper understanding of CNN's mechanism rather than only focusing on the result of different models. The explainable analysis visualizes the loss value and accuracy curves, CAM & Grad-CAM images, and activation maps. It leads to three conclusions: first, as the depth of the model increases, the average loss values of a given epoch will decrease; secondly, the accuracy will increase with the increasing depth; third, the extracted attributes become more abstract in the deeper hidden layers.