Research Article
COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine
@ARTICLE{10.4108/eetel.v8i1.2504, author={Xue Han and Zuojin Hu and William Wang}, title={COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine}, journal={EAI Endorsed Transactions on e-Learning}, volume={8}, number={1}, publisher={EAI}, journal_a={EL}, year={2022}, month={8}, keywords={COVID-19, diagnosis, Wavelet Entropy, Extreme Learning Machine, k-fold cross validation}, doi={10.4108/eetel.v8i1.2504} }- Xue Han
Zuojin Hu
William Wang
Year: 2022
COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine
EL
EAI
DOI: 10.4108/eetel.v8i1.2504
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.
Copyright © 2022 Xue Han et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
