Skip to main content
Springer Nature Link
Log in

COVID-19 Detection Using Chest X-Ray Images with a RegNet Structured Deep Learning Model

  • Conference paper
  • First Online:
Applied Intelligence and Informatics (AII 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1435))

Included in the following conference series:

  • 741 Accesses

Abstract

AI-based medical image processing has made significant progress, and it has a significant impact on biomedical research. Among the imaging variants, Chest x-rays imaging is cheap, simple, and can be used to detect influenza, tuberculosis, and various other illnesses. Researchers discovered that coronavirus spreads through the lungs, causing severe injuries during the COVID19 pandemic. As a result, chest x-rays can be used to detect COVID-19, making it a more robust detection method. In this paper, a RegNet hierarchical deep learning-based model has been proposed to detect COVID-19 positive and negative cases using CXI. The RegNet structure is designed to develop a model with a small number of epochs and parameters. The performance measurement found that the model takes five periods to reach a total accuracy of 98.08%. To test the model, we used two sets of data. The first dataset consists of 1200 COVID-19 positive CXRs and 1,341 COVID-19 negative CXRs, and the second dataset consists of 195 COVID-19 positive CXRs and 2,000 COVID-19 negative CXRs; all of these are publicly available. We obtained precision of 99.02% and 97.13% for these datasets, respectively. As a result of this finding, the proposed approach could be used for mass screening, and, as far as we are aware, the results achieved indicate that this model could be used as a screen guide.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Kaiser, M.S., et al.: iWorkSafe: towards healthy workplaces during COVID-19 with an intelligent pHealth app for industrial settings. IEEE Access 9, 13814–13828 (2021)

    Article  Google Scholar 

  2. Jaiswal, A.K., et al.: Identifying pneumonia in chest X-rays: a deep learning approach. Measurement 145, 511–518 (2019)

    Article  Google Scholar 

  3. Aradhya, V.M., Mahmud, M., Guru, D.S., Agarwal, B., Kaiser, M.S.: One-shot cluster-based approach for the detection of COVID-19 from chest X-ray images. Cogn. Comput. 1–9 (2021). https://doi.org/10.1007/s12559-020-09774-w

  4. Mahmud, M., Kaiser, M.S.: Machine learning in fighting pandemics: a COVID-19 case study. In: Santosh, K., Joshi, A. (eds.) COVID-19: Prediction, Decision-Making, and Its Impacts, vol. 60, pp. 77–81. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9682-7_9

  5. Singh, A.K., Kumar, A., Mahmud, M., Kaiser, M.S., Kishore, A.: COVID-19 Infection detection from chest X-ray images using hybrid social group optimization and support vector classifier. Cogn. Comput. 1–13 (2021). https://doi.org/10.1007/s12559-021-09848-3

  6. Mahmud, M., Kaiser, M.S., McGinnity, T.M., Hussain, A.: Deep learning in mining biological data. Cogn. Comput. 13(1), 1–33 (2021). https://doi.org/10.1007/s12559-020-09773-x

    Article  Google Scholar 

  7. Baltruschat, et al.: Comparison of deep learning approaches for multi-label chest X-ray classification. Sci. Rep. 9(1), 1–10 (2019)

    Article  Google Scholar 

  8. Noor, M.B.T., Zenia, N.Z., Kaiser, M.S., Al Mamun, S., Mahmud, M.: Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and Schizophrenia. Brain Inform. 7(1), 1–21 (2020). https://doi.org/10.1186/s40708-020-00112-2

  9. Ruiz, J., Mahmud, M., Modasshir, M., Kaiser, M.S.: 3D DenseNet ensemble in 4-way classification of Alzheimer’s disease. In: : Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds.) International Conference on Brain Informatics, pp. 85–96. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59277-6_8

  10. Mahmud, M., et al.: A brain-inspired trust management model to assure security in a cloud based IoT framework for neuroscience applications. Cogn. Comput. 10(5), 864–873 (2018). https://doi.org/10.1007/s12559-018-9543-3

    Article  Google Scholar 

  11. Rabby, G., Azad, S., Mahmud, M., Zamli, K.Z., Rahman, M.M.: TeKET: a tree-based unsupervised keyphrase extraction technique. Cogn. Comput. 12(4), 811–833 (2020). https://doi.org/10.1007/s12559-019-09706-3

    Article  Google Scholar 

  12. Kaiser, M.S., et al.: Advances in crowd analysis for urban applications through urban event detection. IEEE Trans. Intell. Transp. Syst. 19(10), 3092–3112 (2017)

    Article  Google Scholar 

  13. Yadav, S.S., Jadhav, S.M.: Deep convolutional neural network based medical image classification for disease diagnosis. J. Big Data 6(1), 1–18 (2019). https://doi.org/10.1186/s40537-019-0276-2

    Article  Google Scholar 

  14. Santosh, K.C., Dhar, M.K., Rajbhandari, R., Neupane, A.: Deep neural network for foreign object detection in chest X-rays. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pp. 538–541. IEEE (2020)

    Google Scholar 

  15. Santosh, K.C.: AI-Driven tools for coronavirus outbreak: need of active learning and cross-population train/test models on multitudinal/multimodal data. J. Med. Syst. 44(5), 1–5 (2020). https://doi.org/10.1007/s10916-020-01562-1

    Article  Google Scholar 

  16. Das, D., Santosh, K.C., Pal, U.: Truncated inception net: COVID-19 outbreak screening using chest X-rays. Phys. Eng. Sci. Med. 43(3), 915–925 (2020). https://doi.org/10.1007/s13246-020-00888-x

    Article  Google Scholar 

  17. Mukherjee, H., Ghosh, S., Dhar, A., Obaidullah, S.M., Santosh, K.C., Roy, K.: Deep neural network to detect COVID-19: one architecture for both CT Scans and Chest X-rays. Appl. Intell. 51(5), 2777–2789 (2020). https://doi.org/10.1007/s10489-020-01943-6

    Article  Google Scholar 

  18. Mukherjee, H., Ghosh, S., Dhar, A., Obaidullah, S.M., Santosh, K.C., Roy, K.: Shallow convolutional neural network for COVID-19 outbreak screening using chest X-rays. Cogn. Comput. (2021). https://doi.org/10.1007/s12559-020-09775-9

  19. Togacar, M., et al.: 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)

    Article  Google Scholar 

  20. Narin, A., et al.: Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. arXiv preprint arXiv: 2003.10849 (2020)

  21. Huang, C., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet 395(10223), 497–506 (2020)

    Article  Google Scholar 

  22. Fang, Y., et al.: Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology 296(2), E115–E117 (2020)

    Article  Google Scholar 

  23. Ng, M.Y., et al.: Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiol.: Cardiothorac. Imaging 2(1), e200034 (2020)

    Google Scholar 

  24. Li, Y., Xia, L.: Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management. Am. J. Roentgenol. 214(6), 1280–1286 (2020)

    Article  Google Scholar 

  25. Ye, Z., Zhang, Y., Wang, Y., Huang, Z., Song, B.: Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review. Eur. Radiol. 30(8), 4381–4389 (2020). https://doi.org/10.1007/s00330-020-06801-0

    Article  Google Scholar 

  26. Zhou, S., Wang, Y., Zhu, T., Xia, L.: CT features of coronavirus disease 2019 (COVID-19) pneumonia in 62 patients in Wuhan, China. Am. J. Roentgenol. 214(6), 1287–1294 (2020)

    Article  Google Scholar 

  27. Bassi, P.R., Attux, R.: A deep convolutional neural network for COVID-19 detection using chest X-rays. arXiv preprint arXiv:2005.01578 (2020)

  28. Majeed, T., Rashid, R., Ali, D., Asaad, A.: COVID-19 detection using CNN transfer learning from X-ray images. medRxiv (2020)

    Google Scholar 

  29. 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. 121, 103792 (2020)

    Article  Google Scholar 

  30. Xu, J., et al.: RegNet: Self-regulated network for image classification. arXiv preprint arXiv:2101.00590 (2021)

  31. Hussain, E., et al.: CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images. Chaos, Solitons Fractals 142, 110495 (2021)

    Article  MathSciNet  Google Scholar 

  32. Chowdhury, M.E., et al.: Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8, 132665–132676 (2020)

    Article  Google Scholar 

  33. Rahman, T., et al.: Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images (2020)

    Google Scholar 

  34. Minaee, S., et al.: Deep-COVID: predicting COVID-19 from chest X-ray images using deep transfer learning. Med. Image Anal. 101794 (2020). https://doi.org/10.1016/j.media.2020.101794

  35. Radosavovic, I., et al.: Designing network design spaces. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10428–10436 (2020)

    Google Scholar 

  36. Aradhya, V.N.M., et al.: One-shot cluster-based approach for the detection of COVID–19 from chest X-ray images. Cogn. Comput. 1–9 (2021). https://doi.org/10.1007/s12559-020-09774-w

  37. Mahmud, M., Kaiser, M.S., Hussain, A., Vassanelli, S.: Applications of deep learning and reinforcement learning to biological data. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2063–2079 (2018)

    Article  MathSciNet  Google Scholar 

  38. Shah, P.M., et al.: Deep GRU-CNN model for COVID-19 detection from chest X-rays data. IEEE Access (2021). https://doi.org/10.1109/ACCESS.2021.3077592

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh

    Md. Kawsher Mahbub, Milon Biswas & Abdul Mozid Miah

  2. University of South Florida, Tampa, USA

    Ahmed Shahabaz

  3. Institute of Information Technology, Jahangimagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

Authors
  1. Md. Kawsher Mahbub
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Milon Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdul Mozid Miah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ahmed Shahabaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. Shamim Kaiser
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Shamim Kaiser .

Editor information

Editors and Affiliations

  1. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  2. Jahangirnagar University, Savar, Dhaka, Bangladesh

    M. Shamim Kaiser

  3. Auckland University of Technology, Auckland, New Zealand

    Nikola Kasabov

  4. Old Dominion University, Norfolk, VA, USA

    Khan Iftekharuddin

  5. Maebashi Institute of Technology, Maebashi, Japan

    Ning Zhong

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mahbub, M.K., Biswas, M., Miah, A.M., Shahabaz, A., Kaiser, M.S. (2021). COVID-19 Detection Using Chest X-Ray Images with a RegNet Structured Deep Learning Model. In: Mahmud, M., Kaiser, M.S., Kasabov, N., Iftekharuddin, K., Zhong, N. (eds) Applied Intelligence and Informatics. AII 2021. Communications in Computer and Information Science, vol 1435. Springer, Cham. https://doi.org/10.1007/978-3-030-82269-9_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-82269-9_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82268-2

  • Online ISBN: 978-3-030-82269-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics