Detection of Pneumonia Based on ResNet Improved by Attention Mechanism

Abstract

In recent years, Covid-19 is one of the major health challenges facing the human population. Due to the highly infectious nature of Covid-19 and the difficulty of detecting symptoms in the early stages, it is definitely necessary to combine X-ray for the diagnosis of pneumonia. Using traditional neural networks such as VGG, ResNet, and DenseNet to diagnose pneumonia based on X-ray images faces a number of difficulties. These models have insufficient spatial information extraction capability and are prone to overfitting on the training set. The attention mechanism is a means to improve model performance by helping the model better extract channel and spatial features from the feature maps. To identify pneumonia more accurately, we combined the ResNet network and CBAM attention mechanism to design the ResNet101-cbam model with a series of data augmentation methods as well as training strategies. We used the same approach to add attention mechanisms to ResNet50, ResNet101 and ResNet152 and tested their performance. The results show that ResNet101-cbam is the best performing model overall. It achieved a recall of 0.8205, a precision of 0.822, and an accuracy of 0.8285 on the test set, while the original pretrained ResNet101 had a precision of 0.7280 and an accuracy of 0.7644. Its performance were better than the more complex model: ResNet152-cbam, a little bit, but the training speed is improved by more than 25%. More importantly, the model with the added attention mechanism effectively overcomes the effects of positive and negative sample imbalance. The ResNet101-cbam model can be used as a medical aid, which can improve diagnostic efficiency and help us better deal with large-scale pneumonia epidemics.