A Novel Hybrid Optimization Enabled Densenet for Covid-19 Classification using CT Images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Instrumentation
Link
https://link.springer.com/content/pdf/10.1007/s11220-023-00434-5.pdf
Reference42 articles.
1. Jiang, Y., Chen, H., Loew, M., & Ko, H. (2020). COVID-19 CT image synthesis with a conditional generative adversarial network. IEEE Journal of Biomedical and Health Informatics, 25(2), 441–452.
2. Guan, W. J., Ni, Z. Y., Hu, Y., Liang, W. H., Ou, C. Q., He, J. X., Liu, L., Shan, H., Lei, C. L., Hui, D. S., & Du, B. (2020). Clinical characteristics of coronavirus disease 2019 in China. New England Journal of Medicine, 382(18), 1708–1720.
3. Hu, S., Gao, Y., Niu, Z., Jiang, Y., Li, L., Xiao, X., Wang, M., Fang, E. F., Menpes-Smith, W., Xia, J., & Ye, H. (2020). Weakly supervised deep learning for covid-19 infection detection and classification from CT images. IEEE Access, 8, 118869–118883.
4. Turkoglu, M. (2021). COVID-19 detection system using chest CT images and multiple kernels-extreme learning machine based on deep neural network. IRBM, 42(4), 207–214.
5. Perumal, V., Narayanan, V., & Rajasekar, S. J. S. (2021). Detection of COVID-19 using CXR and CT images using transfer learning and haralick features. Applied Intelligence, 51(1), 341–358.
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