Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization

Authors

Shui-Hua Wang^{1, 2, #}, Xiaosheng Wu^{1, #}, Yu-Dong Zhang^{1, 4, 5, 6, *,} , Chaosheng Tang^{1, *,} , Xin Zhang^{3, *,}

¹School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, China

²School of Mathematics and Actuarial Science, University of Leicester, Leicester, LE1 7RH, UK

³Department of Medical Imaging, The Fourth People's Hospital of Huai’an, Huai’an, Jiangsu 223002, China

⁴School of Informatics, University of Leicester, Leicester, LE1 7RH, UK

⁵Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Al Jamiʿah, Jeddah 21589, Saudi Arabia

⁶Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Qixing, Guilin 541004, China

^#

Those authors contributed equally to this paper

^*Corresponding authors. Email: yudongzhang@ieee.org; tcs@hpu.edu.cn; 973306782@qq.com

DOI

10.2991/ijcis.d.200828.001How to use a DOI?

Keywords

Wavelet Renyi entropy; three-segment biogeography-based optimization; feedforward neural network; COVID-19; diagnosis

Abstract

Corona virus disease 2019 (COVID-19) is an acute infectious pneumonia and its pathogen is novel and was not previously found in humans. As a diagnostic method for COVID-19, chest computed tomography (CT) is more sensitive than reverse transcription polymerase chain reaction. However, the interpretation of COVID-19 based on chest CT is mainly done manually by radiologists and takes about 5 to 15 minutes for one patient. To shorten the time of interpreting the CT image and improve the reliability of identification of COVID-19. In this paper, a novel chest CT-based method for the automatic detection of COVID-19 was proposed. Our algorithm is a hybrid method composed of (i) wavelet Renyi entropy, (ii) feedforward neural network, and (iii) a proposed three-segment biogeography-based optimization (3SBBO) algorithm. The wavelet Renyi entropy is used to extract the image features. The novel optimization method of 3SBBO can optimize weights, biases of the network, and Renyi entropy order. Finally, we used 296 chest CT images to evaluate the detection performance of our proposed method. In order to reduce randomness and get unbiased result, the 10 runs of 10-fold cross validation are introduced. Experimental outcomes show that our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, and F1.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

TY  - JOUR
AU  - Shui-Hua Wang
AU  - Xiaosheng Wu
AU  - Yu-Dong Zhang
AU  - Chaosheng Tang
AU  - Xin Zhang
PY  - 2020
DA  - 2020/09/17
TI  - Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization
JO  - International Journal of Computational Intelligence Systems
SP  - 1332
EP  - 1344
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200828.001
DO  - 10.2991/ijcis.d.200828.001
ID  - Wang2020
ER  -