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phat 23(1):

Research Article

WE-BA: Covid-19 detection by Wavelet Entropy and Bat Algorithm

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  • @ARTICLE{10.4108/eetpht.9.711,
        author={Wangyang Yu and Yanrong Pei and Shui-Hua Wang and Yu-Dong Zhang},
        title={WE-BA: Covid-19 detection by Wavelet Entropy and Bat Algorithm},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={9},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2023},
        month={10},
        keywords={Covid-19 diagnsosis, wavelet entropy, bat algorithm, feedforward neural network, K-fold cross validation},
        doi={10.4108/eetpht.9.711}
    }
    
  • Wangyang Yu
    Yanrong Pei
    Shui-Hua Wang
    Yu-Dong Zhang
    Year: 2023
    WE-BA: Covid-19 detection by Wavelet Entropy and Bat Algorithm
    PHAT
    EAI
    DOI: 10.4108/eetpht.9.711
Wangyang Yu1, Yanrong Pei2, Shui-Hua Wang1, Yu-Dong Zhang1,*
  • 1: University of Leicester
  • 2: Huai'an Tongji Hospital, Huai'an, Jiangsu, China
*Contact email: yudongzhang@ieee.org

Abstract

Covid-19 is a kind of fast-spreading pneumonia and has dramatically impacted human life and the economy. As early diagnosis is the most effective method to treat patients and block virus transmission, an accurate, automatic, and effective diagnosis method is needed. Our research proposes a machine learning model (WE-BA) using wavelet entropy for feature extraction to reduce the excessive features, one-layer FNNs for classification, 10-fold cross-validation (CV) to reuse the data for the relatively small dataset, and bat algorithm (BA) as a training algorithm. The experiment eventually achieved excellent performance with an average sensitivity of 75.27% ± 3.25%, an average specificity of 75.88% ± 1.89%, an average precision of 75.75% ± 1.06%, an average accuracy of 75.57% ± 1.21%, an average F1 score of 75.47% ± 1.64%, an average Matthews correlation coefficient of 51.20% ± 2.42%, and an average Fowlkes–Mallows index of 75.49% ± 1.64%. The experiments showed that the proposed WE-BA method yielded superior performance to the state-of-the-art methods. The results also proved the potential of the proposed method for the CT image classification task of Covid-19 on a small dataset.

Keywords
Covid-19 diagnsosis, wavelet entropy, bat algorithm, feedforward neural network, K-fold cross validation
Received
2022-04-28
Accepted
2022-08-05
Published
2023-10-17
Publisher
EAI
http://dx.doi.org/10.4108/eetpht.9.711

Copyright © 2023 W. Yu 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.

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