COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine

Xue Han; Zuojin Hu; William Wang

doi:10.4108/eetel.v8i1.2504

Authors

Xue Han Nanjing Normal University of Special Education, China
Zuojin Hu Nanjing Normal University of Special Education, China
William Wang Waynesburg University

DOI:

https://doi.org/10.4108/eetel.v8i1.2504

Keywords:

COVID-19, diagnosis, Wavelet Entropy, Extreme Learning Machine, k-fold cross validation

Abstract

In recent years, COVID-19 has spread rapidly among humans. Chest CT is an effective means of diagnosing COVID-19. However, the diagnosis of CT images still depends on the doctor's visual judgment and medical experience. This takes a certain amount of time and may lead to misjudgment. In this paper, a new algorithm for automatic diagnosis of COVID-19 based on chest CT image data was proposed. The algorithm comprehensively uses WE to extract image features, uses ELM for training, and finally passes k-fold CV validation. After evaluating and detecting performance on 296 chest CT images, our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, F1, MCC and FMI.

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