Hyperparameters Optimization of Deep Convolutional Neural Network for Detecting COVID-19 Using Differential Evolution

Abdelrahman Ezzeldin Nagib (), Mostafa Mohamed Saeed (), Shereen Fathy El-Feky () and Ali Khater Mohamed ()
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Abdelrahman Ezzeldin Nagib: October University for Modern Sciences and Arts (MSA)
Mostafa Mohamed Saeed: October University for Modern Sciences and Arts (MSA)
Shereen Fathy El-Feky: October University for Modern Sciences and Arts (MSA)
Ali Khater Mohamed: October University for Modern Sciences and Arts (MSA)

Chapter Chapter 18 in Decision Sciences for COVID-19, 2022, pp 305-325 from Springer

Abstract: Abstract COVID-19 is one of the most dangerous diseases that appeared during the past 100 years, that caused millions of deaths worldwide. It caused hundreds of billions of losses worldwide as a result of complete business paralysis. This reason has attracted many researchers to attempt to find a suitable treatment for this dreaded virus. The search for a cure is still ongoing, but many researchers around the world have begun to search for the safest ways to detect if a person carries the virus or not. Many researchers resorted to artificial intelligence and machine learning techniques in order to detect whether a person is carrying the virus or not. However, many problems are arising when using these techniques, the most important problem is the optimal selection of the parameter values for these methods, as the choice of these values greatly affects the expected results. In this chapter, Differential Evolution algorithm is used to determine the optimal values for the hyperparameters of Convolutional Neural Networks, as Differential Evolution is one of the most efficient optimization algorithms in the last two decades. The results obtained showed that the use of Differential Evolution in optimizing the hyperparameters of the Convolutional Neural Network was very efficient.

Keywords: Convolutional neural network; COVID-19; Differential Evolution; Hyperparameters Optimization (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-87019-5_18

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DOI: 10.1007/978-3-030-87019-5_18

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