VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-06918-x.pdf
Reference64 articles.
1. Zu ZY, Di Jiang M, Xu PP et al (2020) Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology 296:E15–E25
2. Wu F, Zhao S, Yu B et al (2020) A new coronavirus associated with human respiratory disease in China. Nature 579:265–269. https://doi.org/10.1038/s41586-020-2008-3
3. D’Angelo G, Palmieri F (2020) Discovering genomic patterns in SARS-CoV-2 variants. Int J Intell Syst 35:1680–1698. https://doi.org/10.1002/int.22268
4. Wu Z, McGoogan JM (2020) Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China. JAMA 323:1239. https://doi.org/10.1001/jama.2020.2648
5. Garain A, Basu A, Giampaolo F et al (2021) Detection of COVID-19 from CT scan images: a spiking neural network-based approach. Neural Comput Appl. https://doi.org/10.1007/s00521-021-05910-1
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