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 used to determine the best values for the hyperparameters of the DenseNet121 architecture. To help this architecture to achieve a high level of accuracy in diagnosing COVID-19 through chest x-ray images. The obtained results showed that the proposed approach could classify 98.38% of the test set correctly. To test the efficacy of the GSA in setting the optimum values for the hyperparameters of DenseNet121. The GSA was compared to another approach called SSD-DenseNet121, which depends on the DenseNet121 and the optimization algorithm called social ski driver (SSD). The comparison results demonstrated the efficacy of the proposed GSA-DenseNet121-COVID-19. As it was able to diagnose COVID-19 better than SSD-DenseNet121 as the second was able to diagnose only 94% of the test set. The proposed approach was also compared to another method based on a CNN architecture called Inception-v3 and manual search to quantify hyperparameter values. The comparison results showed that the GSA-DenseNet121-COVID-19 was able to beat the comparison method, as the second was able to classify only 95% of the test set samples. The proposed GSA-DenseNet121-COVID-19 was also compared with some related work. The comparison results showed that GSA-DenseNet121-COVID-19 is very competitive.

在本文中，使用优化算法提出了一种基于混合卷积神经网络（CNN）架构的称为 GSA-DenseNet121-COVID-19 的新方法。使用的 CNN 架构称为 DenseNet121，使用的优化算法称为引力搜索算法 (GSA)。GSA 用于确定 DenseNet121 架构的超参数的最佳值。帮助该架构通过胸部 X 光图像诊断 COVID-19 达到高精度。获得的结果表明，所提出的方法可以正确分类 98.38% 的测试集。测试 GSA 在设置 DenseNet121 超参数最佳值方面的功效。GSA 与另一种称为 SSD-DenseNet121 的方法进行了比较，后者依赖于 DenseNet121 和称为社交滑雪驱动程序 (SSD) 的优化算法。比较结果证明了所提出的 GSA-DenseNet121-COVID-19 的功效。因为它能够比 SSD-DenseNet121 更好地诊断 COVID-19，而第二个只能诊断测试集的 94%。所提出的方法还与另一种基于 CNN 架构（称为 Inception-v3）和手动搜索来量化超参数值的方法进行了比较。比较结果表明，GSA-DenseNet121-COVID-19 能够击败比较方法，因为第二个方法只能对 95% 的测试集样本进行分类。所提出的 GSA-DenseNet121-COVID-19 还与一些相关工作进行了比较。对比结果表明GSA-DenseNet121-COVID-19非常有竞争力。