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COVID-19 Diagnosis by Stationary Wavelet Entropy and Extreme Learning Machine

COVID-19 Diagnosis by Stationary Wavelet Entropy and Extreme Learning Machine

Xue Han, Zuojin Hu, William Wang, Dimas Lima
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 13
ISSN: 2641-6255|EISSN: 2641-6263|EISBN13: 9781683183693|DOI: 10.4018/IJPCH.309952
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MLA

Han, Xue, et al. "COVID-19 Diagnosis by Stationary Wavelet Entropy and Extreme Learning Machine." IJPCH vol.12, no.1 2022: pp.1-13. http://doi.org/10.4018/IJPCH.309952

APA

Han, X., Hu, Z., Wang, W., & Lima, D. (2022). COVID-19 Diagnosis by Stationary Wavelet Entropy and Extreme Learning Machine. International Journal of Patient-Centered Healthcare (IJPCH), 12(1), 1-13. http://doi.org/10.4018/IJPCH.309952

Chicago

Han, Xue, et al. "COVID-19 Diagnosis by Stationary Wavelet Entropy and Extreme Learning Machine," International Journal of Patient-Centered Healthcare (IJPCH) 12, no.1: 1-13. http://doi.org/10.4018/IJPCH.309952

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Abstract

COVID-19 has swept the world and has had great impact on us. Rapid and accurate diagnosis of COVID-19 is essential. Analysis of chest CT images is an effective means. In this paper, an automatic diagnosis algorithm based on chest CT images is proposed. It extracts image features by stationary wavelet entropy (SWE), classifies and trains the input dataset by extreme learning machine (LEM), and finally determines the model through k-fold cross-validation (k-fold CV). By detecting 296 chest CT images of healthy individuals and COVID-19 patients, the algorithm outperforms state-of-the-art methods in sensitivity, specificity, precision, accuracy, F1, MCC, and FMI.

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