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A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images

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  • Burak Gülmez

    (Erciyes University)

Abstract

The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively.

Suggested Citation

  • Burak Gülmez, 2023. "A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images," Annals of Operations Research, Springer, vol. 328(1), pages 617-641, September.
    • Handle: RePEc:spr:annopr:v:328:y:2023:i:1:d:10.1007_s10479-022-05151-y
    DOI: 10.1007/s10479-022-05151-y
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    References listed on IDEAS

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    1. Burak Gülmez & Sinem Kulluk, 2019. "Social Spider Algorithm for Training Artificial Neural Networks," International Journal of Business Analytics (IJBAN), IGI Global, vol. 6(4), pages 32-49, October.
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