An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization.

基于引力搜索优化的 COVID - 19 疾病诊断优化深度学习架构。

• 影响指数: 8.263
• 发表时间: Jan 2021
• 来源期刊: Appl Soft Comput
• DOI: 10.1016/j.asoc.2020.106742
• PMID: 32982615
• 文章类型: Journal Article
• 中科院分区: Q1;Q1
• 作者列表: Ezzat D,Hassanien AE,Ella HA

关键词: Convolutional neural networks; Deep learning; Gravitational search algorithm; Hyperparameters optimization; SARS-CoV-2; Transfer learning

Abstract

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.

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）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。通过胸部X射线图像帮助这种架构在诊断COVID-19时达到很高的准确性。结果表明，该方法可以正确分类98.38％的测试集。测试GSA在为DenseNet121的超参数设定最佳值方面的功效。将GSA与另一种称为SSD-DenseNet121的方法进行了比较，该方法取决于DenseNet121和称为社交滑雪驱动程序（SSD）的优化算法。比较结果证明了拟议的GSA-DenseNet121-COVID-19的功效。因为它能够比SSD-DenseNet121更好地诊断COVID-19，因为第二个只能诊断94%的测试集。所提出的方法还与另一种基于CNN架构的方法进行了比较，称为Inception-v3和手动搜索以量化超参数值。对比结果显示,GSA-DenseNet121-COVID-19能够战胜对比方法,因为第二个能够分类的只有95%的测试集样本。还将拟议的GSA-DenseNet121-COVID-19与一些相关工作进行了比较。比较结果表明，GSA-DenseNet121-COVID-19具有很强的竞争力。