  The new Coronavirus is spreading rapidly, and it has taken the lives of many
people so far. The virus has destructive effects on the human lung, and early
detection is very important. Deep Convolution neural networks are such powerful
tools in classifying images. Therefore, in this paper, a hybrid approach based
on a deep network is presented. Feature vectors were extracted by applying a
deep convolution neural network on the images, and useful features were
selected by the binary differential meta-heuristic algorithm. These optimized
features were given to the SVM classifier. A database consisting of three
categories of images such as COVID-19, pneumonia, and healthy included in 1092
X-ray samples was considered. The proposed method achieved an accuracy of
99.43%, a sensitivity of 99.16%, and a specificity of 99.57%. Our results
demonstrate that the suggested approach is better than recent studies on
COVID-19 detection with X-ray images.
