COVID-19 image classification based on Deep Learning

ABSTRACT

COVID-19 is diagnosed by nucleic acid testing, aided by Computed Tomography. In order to rapidly screen CT images of COVID-19, Squeeze-And-Excitation Network based network model combined with Deep Learning is proposed, which can adapt to learn important parts of the feature channel. Firstly, the feature Squeeze is carried out along the space dimension, and the output dimension matches the number of input feature channels. Secondly, the feature channel learns the feature channel characteristics by capturing the channel dependencies in the previous step. Finally, the weight is updated to model the correlation of feature channels. The Precision, Recall and Specificity were selected to be 92.8%, 92.8% and 93.7%, the Accuracy of the model was 93.24% for the whole sample specificity. Compared with the mainstream model, the experimental results of this model are improved greatly. © 2022 ACM.