Covid-19 Detection by Wavelet Entropy and Self-adaptive PSO
9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022
; 13258 LNCS:125-135, 2022.
Article
in English
| Scopus | ID: covidwho-1899008
ABSTRACT
The rapid global spread of COVID-19 disease poses a huge threat to human health and the global economy. The rapid increase in the number of patients diagnosed has strained already scarce healthcare resources to track and treat Covid-19 patients in a timely and effective manner. The search for a fast and accurate way to diagnose Covid-19 has attracted the attention of many researchers. In our study, a deep learning framework for the Covid-19 diagnosis task was constructed using wavelet entropy as a feature extraction method and a feedforward neural network classifier, which was trained using an adaptive particle swarm algorithm. The model achieved an average sensitivity of 85.14% ± 2.74%, specificity of 86.76% ± 1.75%, precision of 86.57% ± 1.36%, accuracy of 85.95% ± 1.14%, and F1 score of 85.82% ± 1.30%, Matthews correlation coefficient of 71.95 ± 2.26%, and Fowlkes-Mallows Index of 85.83% ± 1.30%. Our experiments validate the usability of wavelet entropy-based feature extraction methods in the medical image domain and show the non-negligible impact of different optimisation algorithms on the models by comparing them with other models. © 2022, Springer Nature Switzerland AG.
COVID-19; Self-adaptive particle swarm optimization; Wavelet entropy; Deep learning; Extraction; Feature extraction; Feedforward neural networks; Health risks; Medical imaging; Particle swarm optimization (PSO); Patient monitoring; Adaptive particle swarm optimizations; Adaptive PSO; Feature extraction methods; Global economies; Healthcare resources; Human health; Learning frameworks; Wavelet entropies; Diagnosis
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022
Year:
2022
Document Type:
Article
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