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COVID-19 Diagnosis by Gray-Level Cooccurrence Matrix and PSO

COVID-19 Diagnosis by Gray-Level Cooccurrence Matrix and PSO

Jiaji Wang, Logan Graham
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 14
ISSN: 2641-6255|EISSN: 2641-6263|EISBN13: 9781683183693|DOI: 10.4018/IJPCH.309118
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MLA

Wang, Jiaji, and Logan Graham. "COVID-19 Diagnosis by Gray-Level Cooccurrence Matrix and PSO." IJPCH vol.12, no.1 2022: pp.1-14. http://doi.org/10.4018/IJPCH.309118

APA

Wang, J. & Graham, L. (2022). COVID-19 Diagnosis by Gray-Level Cooccurrence Matrix and PSO. International Journal of Patient-Centered Healthcare (IJPCH), 12(1), 1-14. http://doi.org/10.4018/IJPCH.309118

Chicago

Wang, Jiaji, and Logan Graham. "COVID-19 Diagnosis by Gray-Level Cooccurrence Matrix and PSO," International Journal of Patient-Centered Healthcare (IJPCH) 12, no.1: 1-14. http://doi.org/10.4018/IJPCH.309118

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Abstract

Three years have passed since the sudden outbreak of COVID-19. From that year, the governments of various countries gradually lifted the measures to prevent and control the pandemic. But the number of new infections and deaths from novel coronavirus infections has not declined. So we still need to identify and research the COVID-19 virus to minimize the damage to society. In this paper, the authors use the gray level cooccurrence matrix for feature extraction and particle swarm optimization algorithm to find the optimal solution. After that, this method is validated by using the more common K fold cross validation. Finally, the results of the experimental data are compared with the more advanced methods. Experimental data show that this method achieves the initial expectation.

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