Research Article
Covid-19 Detection by Wavelet Entropy and Cat Swarm Optimization
@INPROCEEDINGS{10.1007/978-3-030-94182-6_38, author={Wei Wang}, title={Covid-19 Detection by Wavelet Entropy and Cat Swarm Optimization}, proceedings={IoT and Big Data Technologies for Health Care. Second EAI International Conference, IoTCare 2021, Virtual Event, October 18-19, 2021, Proceedings, Part II}, proceedings_a={IOTCARE PART 2}, year={2022}, month={6}, keywords={COVID-19 Wavelet entropy Cat Swarm Optimization Feedforward Neural Network K-fold cross-validation}, doi={10.1007/978-3-030-94182-6_38} }- Wei Wang
Year: 2022
Covid-19 Detection by Wavelet Entropy and Cat Swarm Optimization
IOTCARE PART 2
Springer
DOI: 10.1007/978-3-030-94182-6_38
Abstract
The rapid global spread of COVID-19 poses a huge threat to human security. Accurate and rapid diagnosis is essential to contain COVID-19, and an artificial intelligence-based classification model is an ideal solution to this problem. In this paper, we propose a method based on wavelet entropy and Cat Swarm Optimization to classify chest CT images for the diagnosis of COVID-19 and achieve the best performance among similar methods. The mean and standard deviation of sensitivity is 74.93 ± 2.12, specificity is 77.57 ± 2.25, precision is 76.99 ± 1.79, accuracy is 76.25 ± 1.49, F1-score is 75.93 ± 1.53, Matthews correlation coefficient is 52.54 ± 2.97, Feature Mutual Information is 75.94 ± 1.53.
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