Vol.73, No.2, 2022, pp.4193-4210, doi:10.32604/cmc.2022.031147
Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images
  • Nagwan Abdel Samee1, El-Sayed M. El-Kenawy2,3, Ghada Atteia1,*, Mona M. Jamjoom4, Abdelhameed Ibrahim5, Abdelaziz A. Abdelhamid6,7, Noha E. El-Attar8, Tarek Gaber9,10, Adam Slowik11, Mahmoud Y. Shams12
1 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
2 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
3 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt
4 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
5 Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, 35516, Mansoura, Egypt
6 Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, 11566, Cairo, Egypt
7 Department of Computer Science, College of Computing and Information Technology, Shaqra University, 11961, Saudi Arabia
8 Faculty of Computers and Artificial Intelligence, Benha University, Egypt
9 School of Science, Engineering, and Environment, University of Salford, UK
10 Faculty of Computers and Informatics, Suez Canal University, Ismailia, 41522, Egypt
11 Koszalin University of Technology, Poland
12 Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, 33511, Egypt
* Corresponding Author: Ghada Atteia. Email:
Received 11 April 2022; Accepted 17 May 2022; Issue published 16 June 2022
As corona virus disease (COVID-19) is still an ongoing global outbreak, countries around the world continue to take precautions and measures to control the spread of the pandemic. Because of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals, a rapid, reliable, and automatic detection of COVID-19 is in extreme need to curb the number of infections. By analyzing the COVID-19 chest X-ray images, a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm optimizers. The lung region was segmented from the original chest X-ray images and augmented using various transformation operations. Furthermore, the augmented images were fed into the VGG19 deep network for feature extraction. On the other hand, a feature selection method is proposed to select the most significant features that can boost the classification results. Finally, the selected features were input into an optimized neural network for detection. The neural network is optimized using the proposed hybrid optimizer. The experimental results showed that the proposed method achieved 99.88% accuracy, outperforming the existing COVID-19 detection models. In addition, a deep statistical analysis is performed to study the performance and stability of the proposed optimizer. The results confirm the effectiveness and superiority of the proposed approach.
Covid-19; feature selection; dipper throated optimization; particle swarm optimization; deep learning
Cite This Article
N. Abdel Samee, E. M. El-Kenawy, G. Atteia, M. M. Jamjoom, A. Ibrahim et al., "Metaheuristic optimization through deep learning classification of covid-19 in chest x-ray images," Computers, Materials & Continua, vol. 73, no.2, pp. 4193–4210, 2022.
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