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Community-Acquired Pneumonia Recognition by Wavelet Entropy and Cat Swarm Optimization

journal contribution
posted on 06.05.2022, 10:35 by SH Wang, J Zhou, YD Zhang

Community-acquired pneumonia (CAP) is a type of pneumonia acquired outside the hospital. To recognize CAP more efficiently and more precisely, we propose a novel method—wavelet entropy (WE) is used as the feature extractor, and cat swarm optimization (shortened as CSO) is used to train an artificial neural network (ANN). Our method is abbreviated as WE-ANN-CSO. This proposed WE-ANN-CSO algorithm yields a sensitivity of 91.64 ± 0.99%, a specificity of 90.64 ± 2.11%, a precision of 90.96 ± 1.81%, an accuracy of 91.14 ± 1.12%, an F1 score of 91.29 ± 1.04%, an MCC of 82.31 ± 2.22%, an FMI of 91.29 ± 1.03%, and an AUC of 0.9527. This proposed WE-ANN-CSO algorithm provides better performances than four state-of-the-art approaches.

Funding

Medical Research Council Confidence in Concept Award, UK (MC_PC_17171)

Royal Society International Exchanges Cost Share Award, UK (RP202G0230)

British Heart Foundation Accelerator Award, UK (AA/18/3/34220)

Hope Foundation for Cancer Research, UK (RM60G0680)

Global Challenges Research Fund (GCRF), UK (P202PF11)

Sino-UK Industrial Fund, UK (RP202G0289)

History

Citation

Mobile Netw Appl (2022). https://doi.org/10.1007/s11036-021-01897-0

Author affiliation

School of Computing and Mathematical Sciences, University of Leicester

Version

AM (Accepted Manuscript)

Published in

Mobile Networks and Applications

Publisher

Springer Science and Business Media LLC

issn

1383-469X

eissn

1572-8153

Acceptance date

12/10/2021

Copyright date

2022

Available date

21/02/2023

Language

en