TY - GEN
T1 - Covid-19 Detection by Wavelet Entropy and Self-adaptive PSO
AU - Wang, Wei
AU - Wang, Shui Hua
AU - Górriz, Juan Manuel
AU - Zhang, Yu Dong
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - COVID-19
KW - Self-adaptive particle swarm optimization
KW - Wavelet entropy
UR - http://www.scopus.com/inward/record.url?scp=85132032325&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-06242-1_13
DO - 10.1007/978-3-031-06242-1_13
M3 - Conference Proceeding
AN - SCOPUS:85132032325
SN - 9783031062414
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 125
EP - 135
BT - Artificial Intelligence in Neuroscience
A2 - Ferrández Vicente, José Manuel
A2 - Álvarez-Sánchez, José Ramón
A2 - de la Paz López, Félix
A2 - Adeli, Hojjat
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022
Y2 - 31 May 2022 through 3 June 2022
ER -