  The coronavirus (COVID-19) is currently the most common contagious disease
which is prevalent all over the world. The main challenge of this disease is
the primary diagnosis to prevent secondary infections and its spread from one
person to another. Therefore, it is essential to use an automatic diagnosis
system along with clinical procedures for the rapid diagnosis of COVID-19 to
prevent its spread. Artificial intelligence techniques using computed
tomography (CT) images of the lungs and chest radiography have the potential to
obtain high diagnostic performance for Covid-19 diagnosis. In this study, a
fusion of convolutional neural network (CNN), support vector machine (SVM), and
Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray
image dataset was collected and subjected to high pass filter using a Sobel
filter to obtain the edges of the images. Then these images are fed to CNN deep
learning model followed by SVM classifier with ten-fold cross validation
strategy. This method is designed so that it can learn with not many data. Our
results show that the proposed CNN-SVM with Sobel filtering (CNN-SVM+Sobel)
achieved the highest classification accuracy of 99.02% in accurate detection of
COVID-19. It showed that using Sobel filter can improve the performance of CNN.
Unlike most of the other researches, this method does not use a pre-trained
network. We have also validated our developed model using six public databases
and obtained the highest performance. Hence, our developed model is ready for
clinical application
