Abstract
Coronavirus (COVID-19) is a disease which is spreading rapidly, and nearly 1,436,000 people have been infected in about 200 countries all over the world as of April 2020. It is essential to detect COVID-19 at the earliest stage to care for the infected patients and, moreover, to prevent spreading and protect uninfected people. Deep learning approach, namely, convolutional neural networks (CNNs), requires extensive training data. Due to the recent epidemic, collecting enormous radiographic images in a very short duration is a challenging task. The major issues toward the success of CNN approach is the smaller dataset. Training dataset is scaled, and the results of detecting COVID-19 are boosted by using the proposed 3D-ImpCNN approach. This paper introduces 3D_ImpCNN classification model to categorize the patient affected by COVID. The COVID-19 classification outcomes of the method introduced is analyzed which produced better results when compared against existing methods. Accuracy of 3D-ImpCNN classification method was 96.5%, and moreover this method assists in detecting COVID-19 in a rapid manner.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
WHO: Coronavirus disease 2019 covid-19): situation report 80 (2020)
WHO: Who Director-general’s remarks at the media briefing on 2019-ncov (2020)
WHO: Coronavirus disease (covid-2019) situation reports (2020)
Wu, Z., & McGoogan, J. M. (2020). Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. Jama, 323(13), 1239–1242.
Mahase, E. Coronavirus: covid-19 has killed more people than SARS and MERS combined, despite lower case fatality rate. (2020), 368. https://doi.org/10.1136/bmj.m641 (Published 18 February 2020)
Wang, S., Sun, J., Mehmood, I., Pan, C., Chen, Y., Zhang, Y. D. Cerebral micro-bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling. Concurrency and Computation: Practice and Experience, 32(1), 1–16 (2020)
Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging. 35(5), 1299–1312 (2016)
Waheed, A., Goyal, M., Gupta, D., Khanna, A., Al-Turjman, F., Pinheiro, P.R.: Covid GAN: data augmentation using auxiliary classifier GAN for improved Covid-19 detection. IEEE Access. 8, 91916–91923 (2020)
Ouyang, X., Huo, J., Xia, L., Shan, F., Liu, J., Mo, Z., Shi, F.: Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia. IEEE Trans. Med. Imaging. 39, 2595–2605 (2020)
Roy, S., Menapace, W., Oei, S., Luijten, B., Fini, E., Saltori, C., Demi, L.: Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound. IEEE Trans. Med. Imaging. 39, 2676–2687 (2020)
Jamshidi, M., Lalbakhsh, A., Talla, J., Peroutka, Z., Hadjilooei, F., Lalbakhsh, P., Sabet, A.: Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment. IEEE Access. 8, 109581–109595 (2020)
Shuja, J., Alanazi, E., Alasmary, W. et al. COVID-19 open-source data sets: a comprehensive survey. Appl Intell (2020). https://doi.org/10.1007/s10489-020-01862-6
Han, Z., Wei, B., Hong, Y., Li, T., Cong, J., Zhu, X., Wei, H., Zhang, W.: Accurate screening of COVID-19 using attention based deep 3D multiple instance learning. IEEE Trans. Med. Imaging. 39, 2584–2594 (2020)
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 chapter
Cite this chapter
Subramaniyan, M., Sampathkumar, A., Jain, D.K., Ramachandran, M., Patan, R., Kumar, A. (2021). Deep Learning Approach Using 3D-ImpCNN Classification for Coronavirus Disease. In: Al-Turjman, F. (eds) Artificial Intelligence and Machine Learning for COVID-19. Studies in Computational Intelligence, vol 924. Springer, Cham. https://doi.org/10.1007/978-3-030-60188-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-60188-1_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60187-4
Online ISBN: 978-3-030-60188-1
eBook Packages: Computer ScienceComputer Science (R0)