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Epileptic Seizure Detection Using CNN Read chapter Read first chapter

2021 | OriginalPaper | Chapter

CovidNet: A Light-Weight CNN for the Detection of COVID-19 Using Chest X-Ray Images

Authors : Tejalal Choudhary, Aditi Godbole, Vaibhav Gattyani, Aditya Gurnani, Aditi Verma, Aditya Babar

Published in: Advanced Computing

Publisher: Springer Singapore

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Abstract

Corona virus more popularly known as COVID-19 is an extremely virulent strain from the Corona virus family of viruses and their origin is attributed to bats and civet cats. Currently, there is no cure for this virus nor are there any vaccines available to prevent this. Chest X-ray images are used for diagnosing the presence of this virus in the human body. Chest X-rays can be diagnosed only by expert radiotherapists for evaluation. Thus, the development of a system that would detect whether a person is infected by the Corona virus or not without any delay would be very helpful for people as well as doctors. In this research article, we proposed a novel deep learning model named CovidNet to detect the presence of Corona virus in a human body. We performed extensive experiments on the proposed model and pre-trained models, and the experiments show that the proposed model outperformed other pre-trained models. The proposed CovidNet model achieved best testing accuracy of 98.5%.

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Literature
1.
2.
3.
4.
5.
6.
7.
Ghoshal, B., Tucker, A.: Estimating uncertainty and interpretability in deep learning for coronavirus (Covid-19) detection. arXiv preprint arXiv:​2003.​10769 (2020)
8.
9.
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
10.
Hall, L.O., Paul, R., Goldgof, D.B., Goldgof, G.M.: Finding Covid-19 from chest x-rays using deep learning on a small dataset. arXiv preprint arXiv:​2004.​02060 (2020)
11.
Hemdan, E.E.D., Shouman, M.A., Karar, M.E.: COVIDX-Net: a framework of deep learning classifiers to diagnose Covid-19 in x-ray images. arXiv preprint arXiv:​2003.​11055 (2020)
12.
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:​1704.​04861 (2017)
13.
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:​1502.​03167 (2015)
14.
15.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
16.
Maghdid, H.S., Asaad, A.T., Ghafoor, K.Z., Sadiq, A.S., Khan, M.K.: Diagnosing Covid-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms. arXiv preprint arXiv:​2004.​00038 (2020)
17.
Narin, A., Kaya, C., Pamuk, Z.: Automatic detection of coronavirus disease (Covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv:​2003.​10849 (2020)
18.
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information processing Systems, pp. 91–99 (2015)
19.
Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. 19, 221–248 (2017)CrossRef
20.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Published as a Conference Paper at ICLR (2015)
21.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetMATH
22.
Metadata
Title
CovidNet: A Light-Weight CNN for the Detection of COVID-19 Using Chest X-Ray Images
Authors
Tejalal Choudhary
Aditi Godbole
Vaibhav Gattyani
Aditya Gurnani
Aditi Verma
Aditya Babar
Copyright Year
2021
Publisher
Springer Singapore
Book
Advanced Computing

Print ISBN: 978-981-16-0400-3

Electronic ISBN: 978-981-16-0401-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-16-0401-0_13

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