Automatic detection of COVID-19 chest X-ray based on Convolution Neural Network

Ye, Qing Tan ZeXian

Currently, chest X-rays, as one of the auxiliary diagnostic methods for COVID-19, play an important role in the detection of COVID-19. In this paper, we propose an automatic detection method of COVID-19 chest X-ray based on Convolution Neural Network. In this method, we first get an image of the chest by X-ray, and then we preprocess the chest X-rays, and then send the preprocessed X-ray images to the convolutional neural network for feature extraction and classification, and finally we were able to diagnose whether COVID-19 or not. We test the three types of models (Inception V3, ResNet and DenseNet) under different layers, and finally propose the automatic detection method of COVID-19 chest X-ray. After a large number of experiments, the highest accuracy rate of COVID-19 detection is 98.059%. Compared with other studies in the same period, our accuracy rate is higher, and it is very fast. This indicates that our proposed method can be used as an auxiliary detection for COVID-19.