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WACPN: A Neural Network for Pneumonia Diagnosis

Shui-Hua Wang1, Muhammad Attique Khan2, Ziquan Zhu1, Yu-Dong Zhang1,*
1 School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK
2 Department of Computer Science, HITEC University Taxila, Taxila, Pakistan
* Corresponding Author: Yu-Dong Zhang. Email:

Computer Systems Science and Engineering 2023, 45(1), 21-34. https://doi.org/10.32604/csse.2023.031330

Received 14 April 2022; Accepted 17 May 2022; Issue published 16 August 2022

Abstract

Community-acquired pneumonia (CAP) is considered a sort of pneumonia developed outside hospitals and clinics. To diagnose community-acquired pneumonia (CAP) more efficiently, we proposed a novel neural network model. We introduce the 2-dimensional wavelet entropy (2d-WE) layer and an adaptive chaotic particle swarm optimization (ACP) algorithm to train the feed-forward neural network. The ACP uses adaptive inertia weight factor (AIWF) and Rossler attractor (RA) to improve the performance of standard particle swarm optimization. The final combined model is named WE-layer ACP-based network (WACPN), which attains a sensitivity of 91.87 ± 1.37%, a specificity of 90.70 ± 1.19%, a precision of 91.01 ± 1.12%, an accuracy of 91.29 ± 1.09%, F1 score of 91.43 ± 1.09%, an MCC of 82.59 ± 2.19%, and an FMI of 91.44 ± 1.09%. The AUC of this WACPN model is 0.9577. We find that the maximum deposition level chosen as four can obtain the best result. Experiments demonstrate the effectiveness of both AIWF and RA. Finally, this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models. Our model will be distributed to the cloud computing environment.

Keywords

Wavelet entropy; community-acquired pneumonia; neural network; adaptive inertia weight factor; rossler attractor; particle swarm optimization

Cite This Article

S. Wang, M. A. Khan, Z. Zhu and Y. Zhang, "Wacpn: a neural network for pneumonia diagnosis," Computer Systems Science and Engineering, vol. 45, no.1, pp. 21–34, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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