论文标题
GSA-DENSENET121-COVID-19：一种基于重力搜索优化算法的杂交深度学习结构，用于诊断Covid-19疾病
GSA-DenseNet121-COVID-19: a Hybrid Deep Learning Architecture for the Diagnosis of COVID-19 Disease based on Gravitational Search Optimization Algorithm
论文作者
论文摘要
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）结构的新方法，称为GSA-DENSENET121-COVID-19。使用的CNN架构称为Densenet121，使用的优化算法称为重力搜索算法（GSA）。 GSA适应了Densenet121体系结构的超参数的最佳值，并通过胸部X射线图像分析来诊断Covid-19疾病的高度准确性。获得的结果表明，所提出的方法能够正确对测试集的98％进行分类。为了测试GSA在设置Densenet121超参数的最佳值方面的功效，将其与另一种称为社交滑雪驱动器（SSD）的优化算法进行了比较。比较结果表明，与SSD-Densenet121相比，提出的GSA-Densenet121-CoVID-19及其更好地诊断Covid-19疾病的能力的功效，因为第二个仅诊断出测试集的94％。同时，将提出的方法与基于CNN架构的方法进行了比较，称为Inception-V3和用于确定超参数值的手动搜索方法。比较结果表明，GSA-Densenet121能够击败另一种方法，因为第二种仅能对95％的测试集样品进行分类。
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.
