Covid-19 Detection by Wavelet Entropy and Cat Swarm Optimization
2nd International Conference on IoT and Big Data Technologies for HealthCare, IoTCare 2021
; 415 LNICST:479-487, 2022.
Article
in English
| Scopus | ID: covidwho-1930261
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. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
Cat Swarm Optimization; COVID-19; Feedforward Neural Network; K-fold cross-validation; Wavelet entropy; Computer aided diagnosis; Computerized tomography; Entropy; Feedforward neural networks; Machine learning; Chest CT; Classification models; CT Image; Human securities; Ideal solutions; K fold cross validations; Performance; Swarm optimization; Wavelet entropies
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2nd International Conference on IoT and Big Data Technologies for HealthCare, IoTCare 2021
Year:
2022
Document Type:
Article
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