Abstract
Deep learning (DL) has potential in the diagnosis of novel coronavirus disease (COVID-19). Nevertheless, the effectiveness of DL models varies when applied to different datasets and cross-datasets. This paper proposes optimized neural architecture search network (NASNet) for COVID-19 diagnosis. Two forms of NASNet models, namely NASNet-Mobile and NASNet-Large are applied to a dataset of 3411 computed tomography (CT) lung images freely available on GitHub repository. For the experimentation, 85% of the total samples are used for training, while the remaining are used for testing. The training and testing losses and the classification accuracies are varied with respect to the number of epochs. Results show that at an epoch of 15, NASNet-Mobile has an accuracy, a recall and an area under the receiver operating characteristics curve (AUC) of 82.42%, 78.16% and 91.00%, respectively. On the other hand, NASNet-Large has an accuracy, a recall and an AUC of 81.06%, 80.43% and 89.00%, respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lurie, N., Saville, M., Hatchett, R., Halton, J.: Developing Covid-19 vaccines at pandemic speed. New Engl. J. Med. 382(21), 1969–1973 (2020)
Mondal, M.R.H., Bharati, S., Podder, P., Podder, P.: Data analytics for novel coronavirus disease. Inform. Med. Unlocked, 100374 (2020)
Bharati, S.: How artificial intelligence impacts businesses in the period of pandemics? J. Int. Acad. Case Stud 26(5), 1–2 (2020)
Kundu, S., Elhalawani, H., Gichoya, J.W., Kahn, C.E: How Might AI and Chest Imaging Help Unravel COVID-19’s Mysteries? Radiological Society of North America (2020)
McCall, B.: COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. Lancet Digit. Health 2(4), e166–e167 (2020)
Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. arXiv preprint arXiv:161101578 (2016)
Rendón, A., Martınez, F., Hernández, C.: Deep regression model for predictive control in a vegetable waste carbonization plant. Contemp. Eng. Sci. 10(21), 1047–1055 (2017)
Radhika, K., Devika, K., Aswathi, T., Sreevidya, P., Sowmya, V., Soman, K.P.: Performance analysis of NASNet on unconstrained ear recognition. In: Nature Inspired Computing for Data Science. Springer, pp 57–82 (2020)
Kassani, S.H., Kassasni, P.H., Wesolowski, M.J., Schneider, K.A.: Deters R automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: A Machine Learning-Based Approach (2020). arXiv preprint arXiv:200410641
Acar, E., ŞAhİN, E., Yilmaz, İ.: Improving effectiveness of different deep learning-based models for detecting COVID-19 from computed tomography (CT) images. medRxiv (2020)
Martínez, F., Martínez, F., Jacinto, E.: Performance evaluation of the NASNet convolutional network in the automatic identification of COVID-19. Int. J. Adv. Sci. Eng. Inform. Technol. 10(2), 662 (2020)
Bharati, S., Podder, P., Mondal, M.R.H.: Artificial neural network based breast cancer screening: a comprehensive review. Int. J. Comput. Inf. Syst. Ind. Manage. Appl. 12, 125–137 (2020)
Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., Acharya, U.R.: Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 103792 (2020)
Mahmud, T., Rahman, M.A., Fattah, S.A.: CovXNet: a multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization. Comput. Biol. Med. 122, 103869 (2020)
Khan, A.I., Shah, J.L., Bhat, M.M.: Coronet: a deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput. Meth. Programs Biomed. 196, 105581 (2020)
Toğaçar, M., Ergen B, Cömert, Z.: COVID-19 detection using deep learning models to exploit social mimic optimization and structured chest X-ray images using fuzzy color and stacking approaches. Comput. Biol. Med. 121, 103805 (2020)
Nisar, Z.: COVID-19. (2020). https://github.com/zeeshannisar/COVID-19. Accessed 12 Oct 2020
Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE conference on computer vision and pattern. pp 8697–8710 (2018)
Bharati, S., Podder, P., Mondal, M.R.H.: Hybrid deep learning for detecting lung diseases from X-ray images. Inform. Med. Unlocked, 20 100391 (2020)
Podder, P., Khamparia, A., Mondal, M.R.H., Rahman, M.A., Bharati, S.: Forecasting the Spread of COVID-19 and ICU Requirements. Preprints, 2021030447 (2021). https://doi.org/10.20944/preprints202103.0447.v1
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bharati, S., Podder, P., Mondal, M.R.H., Gandhi, N. (2021). Optimized NASNet for Diagnosis of COVID-19 from Lung CT Images. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_59
Download citation
DOI: https://doi.org/10.1007/978-3-030-71187-0_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71186-3
Online ISBN: 978-3-030-71187-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)