X-ray Images Detection of COVID-19 Based on Deepwise Separable DenseNet

Abstract

The Coronavirus Disease (COVID-19) is spreading worldwide. X-ray imaging plays an important role in the diagnosis of COVID-19. In order to help doctors diagnose COVID-19 effectively, we proposed a novel model (DS-DenseNet), which based on depth separable dense. By adding an improved depth separable convolution layer, we reduced the amount of parameters and make the model lighter. In the viral pneumonia, COVID-19 and normal lung, 2905 sets of chest X-ray images were collected, and the restricted contrast limited adaptive histogram equalization (CLAHE) algorithm was applied to preprocess the images and the preprocessed images were input into the model. Meanwhile, SDensenet, VGG16, Resnet18, Resnet34 and Densenet121 were introduced as baseline models. Compared with Resnet34, the sensitivity, accuracy and specificity of DS-Densenet are increased by 2.5%, 2.0% and 1.5% respectively; compared with SDensenet, the number of parameters is reduced by 44.0%, but the effect is not reduced. The experimental results show that the depth separable convolution can effectively reduce the model parameters, and the proposed DS-Densenet has a good classification effect, which has a certain significance for the auxiliary diagnosis of COVID-19.