Open Access iconOpen Access

ARTICLE

crossmark

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

José Escorcia-Gutierrez1,*, Margarita Gamarra1, Roosvel Soto-Diaz2, Safa Alsafari3, Ayman Yafoz4, Romany F. Mansour5

1 Departament of Computational Science and Electronic, Universidad de la Costa, CUC, Barranquilla, 080002, Colombia
2 Biomedical Engineering Program, Universidad Simón Bolívar, Barranquilla, 080002, Colombia
3 Department of Computer Science and AI, Faculty of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
4 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
5 Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt

* Corresponding Author: José Escorcia-Gutierrez. Email: email

Computers, Materials & Continua 2023, 75(3), 5255-5270. https://doi.org/10.32604/cmc.2023.033731

Abstract

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological investigations, computer-aided diagnostic tools are implemented to reduce intra- and inter-observer variability. Using lately industrialized Artificial Intelligence (AI) algorithms and radiological techniques to diagnose and classify disease is advantageous. The current study develops an automatic identification and classification model for CXR pictures using Gaussian Filtering based Optimized Synergic Deep Learning using Remora Optimization Algorithm (GF-OSDL-ROA). This method is inclusive of preprocessing and classification based on optimization. The data is preprocessed using Gaussian filtering (GF) to remove any extraneous noise from the image’s edges. Then, the OSDL model is applied to classify the CXRs under different severity levels based on CXR data. The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work. OSDL model, applied in this study, was validated using the COVID-19 dataset. The experiments were conducted upon the proposed OSDL model, which achieved a classification accuracy of 99.83%, while the current Convolutional Neural Network achieved less classification accuracy, i.e., 98.14%.

Keywords


Cite This Article

APA Style
Escorcia-Gutierrez, J., Gamarra, M., Soto-Diaz, R., Alsafari, S., Yafoz, A. et al. (2023). Optimal synergic deep learning for COVID-19 classification using chest x-ray images. Computers, Materials & Continua, 75(3), 5255-5270. https://doi.org/10.32604/cmc.2023.033731
Vancouver Style
Escorcia-Gutierrez J, Gamarra M, Soto-Diaz R, Alsafari S, Yafoz A, Mansour RF. Optimal synergic deep learning for COVID-19 classification using chest x-ray images. Comput Mater Contin. 2023;75(3):5255-5270 https://doi.org/10.32604/cmc.2023.033731
IEEE Style
J. Escorcia-Gutierrez, M. Gamarra, R. Soto-Diaz, S. Alsafari, A. Yafoz, and R.F. Mansour "Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images," Comput. Mater. Contin., vol. 75, no. 3, pp. 5255-5270. 2023. https://doi.org/10.32604/cmc.2023.033731



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 605

    View

  • 354

    Download

  • 0

    Like

Share Link