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COVID-19 Detection via Wavelet Entropy and Biogeography-Based Optimization

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COVID-19: Prediction, Decision-Making, and its Impacts

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 60))

Abstract

Since the end of 2019, the COVID-19 virus has swept the world, bringing great impact to various fields and gaining wide attention from all walks of life. COVID-19 is known to have a long incubation period. Thus, in terms of virus detection, it’s very time consuming and labor-intensive. Artificial intelligence can analyze CT scan images and assist in detection of patients, which is of great help to realize rapid, effective and safe detection of COVID-19. In this research, a dataset of 132 samples was collected from the Fourth People’s Hospital of Huai’an City. One part of 66 patients with novel coronavirus pneumonia and the other part of 66 healthy people. This experiment uses Wavelet Entropy as a feature extraction method, K-fold as a validation method that reports unbiased performances, Biogeography-based Optimization as a training algorithm and Single-hidden-layer as a classifier. The proposed model achieves good performance with mean sensitivity of 72.97 ± 2.96%, specificity of 74.93 ± 2.39%, precision of 74.48 ± 1.34%, accuracy of 73.95 ± 0.98%, F1 score of 73.66 ± 1.33% and Matthews correlation coefficient of 47.99 ± 2.00%. The results confirmed that this artificial intelligence model can well complete the detection task of COVID-19 via Wavelet Entropy and Biogeography-based Optimization.

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Author information

Authors and Affiliations

  1. School of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Xujing Yao & Ji Han

  2. Department of Medical Imaging, The Fourth People’s Hospital of Huai’an, Huai’an, Jiangsu, 223002, China

    Xujing Yao

  3. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, People’s Republic of China

    Ji Han

Authors
  1. Xujing Yao
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  2. Ji Han
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Correspondence to Xujing Yao .

Editor information

Editors and Affiliations

  1. The University of South Dakota, Vermillion, SD, USA

    K.C. Santosh

  2. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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Yao, X., Han, J. (2021). COVID-19 Detection via Wavelet Entropy and Biogeography-Based Optimization. In: Santosh, K., Joshi, A. (eds) COVID-19: Prediction, Decision-Making, and its Impacts. Lecture Notes on Data Engineering and Communications Technologies, vol 60. Springer, Singapore. https://doi.org/10.1007/978-981-15-9682-7_8

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