General Adversarial Networks: A Tool to Detect the Novel Coronavirus from CT Scans-Reference-Cited by-同舟云学术

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General Adversarial Networks: A Tool to Detect the Novel Coronavirus from CT Scans

Published:2022 Issue: Volume: Page:217-230
ISSN:1876-1100
Container-title:International Conference on Artificial Intelligence for Smart Community
language:
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Author:

Shriram R.,Kumar T. R. Kaushek,Samuktha V.,Karthika R.

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-2183-3_21

Reference17 articles.

1. Ardakani A, Kanafi A, Acharya A, Rajendra U, Nazanin K, Afshin M (2020) Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: results of 10 convolutional neural networks. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2020.103795

2. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, pp 1800–1807. https://doi.org/10.1109/CVPR.2017.195

3. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), Boston, MA, pp 1–9. https://doi.org/10.1109/CVPR.2015.7298594

4. Polsinelli M, Cinque L, Placidi G (2020) A light CNN for detecting COVID-19 from CT scans of the chest. https://doi.org/10.1016/j.patrec.2020.10.001

5. Waheed A, Goyal M, Gupta D, Khanna A, Al-Turjman F, Pinheiro PR (2020) CovidGAN: data augmentation using auxiliary classifier GAN for improved Covid-19 detection. IEEE Access 8:91916–91923. https://doi.org/10.1109/ACCESS.2020.2994762

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