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Classification of Covid-19 using Differential Evolution Chaotic Whale Optimization based Convolutional Neural Network
Abstract
COVID-19, also known as the Coronavirus disease-2019, is an transferrable disease that spreads rapidly, affecting countless individuals and leading to fatalities in this worldwide pandemic. The precise and swift detection of COVID-19 plays a crucial role in managing the pandemic's dissemination. Additionally, it is necessary to recognize COVID-19 quickly and accurately by investigating chest x-ray images. This paper proposed a Differential Evolution Chaotic Whale Optimization Algorithm (DECWOA) based Convolutional Neural Network (CNN) method for identifying and classifying COVID-19 chest X-ray images. The DECWOA based CNN model improves the accuracy and convergence speed of the algorithm. This method is evaluated {by} Chest X-Ray (CXR) dataset and attains better results in terms of accuracy, precision, sensitivity, specificity, and F1-score values of about 99.89}%, 99.83%, 99.81%, 98.92%, and 99.26% correspondingly. The result shows that the proposed DECWOA based CNN model provides accurate and quick identification and classification of COVID-19 compared to existing techniques like ResNet50, VGG-19, and Multi-Model Fusion of Deep Transfer Learning (MMF-DTL) models.
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Authors (5)
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D.p. Manoj Kumar
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Sujata N. Patil
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Parameshachari Bidare Divakarachari
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R. Suganthi
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Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.12694/scpe.v25i3.2691
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Scalable Computing: Practice and Experience
no. 25,
pages 1387 - 1401,
ISSN: 1895-1767 - Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Manoj Kumar D., Patil S. N., Bidare Divakarachari P., Falkowski-Gilski P., Suganthi R.: Classification of Covid-19 using Differential Evolution Chaotic Whale Optimization based Convolutional Neural Network// Scalable Computing: Practice and Experience -Vol. 25,iss. 3 (2024), s.1387-1401
- DOI:
- Digital Object Identifier (open in new tab) 10.12694/scpe.v25i3.2691
- Sources of funding:
-
- COST_FREE
- Verified by:
- Gdańsk University of Technology
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