  In this paper, a novel approach called GSA-DenseNet121-COVID-19 based on a
hybrid convolutional neural network (CNN) architecture is proposed using an
optimization algorithm. The CNN architecture that was used is called
DenseNet121 and the optimization algorithm that was used is called the
gravitational search algorithm (GSA). The GSA is adapted to determine the best
values for the hyperparameters of the DenseNet121 architecture, and to achieve
a high level of accuracy in diagnosing COVID-19 disease through chest x-ray
image analysis. The obtained results showed that the proposed approach was able
to correctly classify 98% of the test set. To test the efficacy of the GSA in
setting the optimum values for the hyperparameters of DenseNet121, it was
compared to another optimization algorithm called social ski driver (SSD). The
comparison results demonstrated the efficacy of the proposed
GSA-DenseNet121-COVID-19 and its ability to better diagnose COVID-19 disease
than the SSD-DenseNet121 as the second was able to diagnose only 94% of the
test set. As well as, the proposed approach was compared to an approach based
on a CNN architecture called Inception-v3 and the manual search method for
determining the values of the hyperparameters. The results of the comparison
showed that the GSA-DenseNet121 was able to beat the other approach, as the
second was able to classify only 95% of the test set samples.
