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COVID-19 Diagnosis by Multiple-Distance Gray-Level Cooccurrence Matrix and Genetic Algorithm

COVID-19 Diagnosis by Multiple-Distance Gray-Level Cooccurrence Matrix and Genetic Algorithm

Xiaoyan Jiang, Mackenzie Brown, Hei-Ran Cheong, Zuojin Hu
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 14
ISSN: 2641-6255|EISSN: 2641-6263|EISBN13: 9781683183693|DOI: 10.4018/IJPCH.309951
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MLA

Jiang, Xiaoyan, et al. "COVID-19 Diagnosis by Multiple-Distance Gray-Level Cooccurrence Matrix and Genetic Algorithm." IJPCH vol.12, no.1 2022: pp.1-14. http://doi.org/10.4018/IJPCH.309951

APA

Jiang, X., Brown, M., Cheong, H., & Hu, Z. (2022). COVID-19 Diagnosis by Multiple-Distance Gray-Level Cooccurrence Matrix and Genetic Algorithm. International Journal of Patient-Centered Healthcare (IJPCH), 12(1), 1-14. http://doi.org/10.4018/IJPCH.309951

Chicago

Jiang, Xiaoyan, et al. "COVID-19 Diagnosis by Multiple-Distance Gray-Level Cooccurrence Matrix and Genetic Algorithm," International Journal of Patient-Centered Healthcare (IJPCH) 12, no.1: 1-14. http://doi.org/10.4018/IJPCH.309951

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Abstract

COVID-19 is extremely contagious and has brought serious harm to the world. Many researchers are actively involved in the study of rapid and reliable diagnostic methods for COVID-19. The study proposes a novel approach to COVID-19 diagnosis. The multiple-distance gray-level co-occurrence matrix (MDGLCM) was used to analyze chest CT images, the GA algorithm was used as an optimizer, and the feedforward neural network was used as a classifier. The results of 10 runs of 10-fold cross-validation show that the proposed method has a sensitivity of 83.38±1.40, a specificity of 81.15±2.08, a precision of 81.59±1.57, an accuracy of 82.26±0.96, an F1-score of 82.46±0.88, an MCC of 64.57±1.90, and an FMI of 82.47±0.88. The proposed MDGLCM-GA-based COVID-19 diagnosis method outperforms the other six state-of-the-art methods.

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