Skip to main content
SpringerLink
Log in

Efficient Pneumonia Detection Method and Implementation in Chest X-ray Images Based on a Neuromorphic Spiking Neural Network

  • Conference paper
  • First Online:
Computational Collective Intelligence (ICCCI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13501))

Included in the following conference series:

Abstract

Deep Learning has solved numerous problems in image recognition and information processing, and is currently being employed in tackling the coronavirus disease (COVID-19) which has become a pandemic. Incisively, deep learning models are utilized in diagnosis systems as a method to detect COVID-19 related pneumonia by analyzing lung X-ray images of patients. The accuracy of this method is in the range of 80–90%. However, it is computationally complex, requires high power, and has low energy efficiency. Consequently, it is not suitable a diagnosis/detection method to be deployed on the edge. In this paper, we propose an efficient pneumonia (COVID-19) detection method and implementation in chest x-ray images based on a neuromorphic spiking neural network. This method is implemented on our previously proposed AI-enabled real-time biomedical system AIRBiS (AIRBiS project: u-aizu.ac.jp/misc/benablab/airbis.html) which is based on a high-performance low-power re-configurable AI-chip for inference, and an interactive user interface for effective operation and monitoring. The evaluation results show that the proposed method achieves 92.1%, and 80.7% detection accuracy of pneumonia (i.e., COVID-19) over-collected test data.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abdallah, A.B., Huang, H., Dang, N.K., Song, J.: AI Processor: Japanese Patent Application Laid-Open No. 2020–194733 (2020)

    Google Scholar 

  2. Abdulkareem, M., Petersen, S.E.: The promise of AI in detection, diagnosis, and epidemiology for combating COVID-19: beyond the hype. Front. Artif. Intell. 14(4), 652669 (2021). https://doi.org/10.3389/frai.2021.652669, www.frontiersin.org/article/10.3389/frai.2021.652669

  3. Ahmed, I., Ahmad, M., Rodrigues, J.J., Jeon, G., Din, S.: A deep learning-based social distance monitoring framework for COVID-19. In: Sustainable Cities and Society, vol. 65, p. 102571 (2021)

    Google Scholar 

  4. Ai, T., et al.: Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in china: a report of 1014 cases. Radiology 296(2), 32–40 (2020)

    Article  Google Scholar 

  5. Albahli, S.: Efficient GAN-based chest radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia. Int. J. Med. Sci. 17(10), 1439 (2020)

    Article  Google Scholar 

  6. Bekolay, T., et al.: Nengo: a python tool for building large-scale functional brain models. Front. Neuroinform. 7, 48 (2014)

    Article  Google Scholar 

  7. Chowdhury, M.E.H., et al.: Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8, 132665–132676 (2020). https://doi.org/10.1109/ACCESS.2020.3010287

    Article  Google Scholar 

  8. Datta, G., Kundu, S., Beerel, P.A.: Training energy-efficient deep spiking neural networks with single-spike hybrid input encoding. Computing Research Repository. CORR abs/2107.12374 (2021)

    Google Scholar 

  9. Dave, R., Gupta, R.: Data quality and network considerations for mobile contact tracing and health monitoring. Front. Digit. Health 3 (2021). https://doi.org/10.3389/fdgth.2021.590194

  10. Dhaka, V.S., Rani, G., Oza, M.G., Sharma, T., Misra, A.: A deep learning model for mass screening of COVID-19. Int. J. Imaging Syst. Technol. 31(2), 483–498 (2021)

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Garain, A., Basu, A., Giampaolo, F., Velasquez, J.D., Sarkar, R.: Detection of COVID-19 from CT scan images: a spiking neural network-based approach. Neural Comput. Appl. 33(19), 12591–12604 (2021). https://doi.org/10.1007/s00521-021-05910-1

    Article  Google Scholar 

  13. Ghani, A., Aina, A., See, C.H., Yu, H., Keates, S.: Accelerated diagnosis of novel coronavirus (COVID-19)—computer vision with convolutional neural networks (CNNs). Electronics 11(7), 1148 (2022). https://doi.org/10.3390/electronics11071148

    Article  Google Scholar 

  14. Goel, G., Gondhalekar, A., Qi, J., Zhang, Z., Cao, G., Feng, W.: Computecovid19+: accelerating Covid-19 diagnosis and monitoring via high-performance deep learning on CT images. In: 50th International Conference on Parallel Processing, pp. 1–11 (2021)

    Google Scholar 

  15. Ikechukwu, O.M.: On the design of adaptive digital neuromorphic system, Ph. D. thesis, University of Aizu (2022)

    Google Scholar 

  16. Maghded, H.S., Ghafoor, K.Z., Sadiq, A.S., Curran, K., Rawat, D.B., Rabie, K.: A novel AI-enabled framework to diagnose coronavirus Covid-19 using smartphone embedded sensors: design study. In: 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), pp. 180–187 (2020). https://doi.org/10.1109/IRI49571.2020.00033

  17. Meng, L., et al.: A deep learning prognosis model help alert for Covid-19 patients at high-risk of death: a multi-center study. IEEE J. Biomed. Health Inform. 24(12), 3576–3584 (2020). https://doi.org/10.1109/JBHI.2020.3034296

    Article  Google Scholar 

  18. Mozafari, M., Ganjtabesh, M., Nowzari-Dalini, A., Masquelier, T.: SpykeTorch: efficient simulation of convolutional spiking neural networks with at most one spike per neuron. Front. Neurosci. 13, 625 (2019)

    Google Scholar 

  19. Muhammad, G., Hossain, M.S.: A deep-learning-based edge-centric COVID-19-like pandemic screening and diagnosis system within a B5G framework using blockchain. IEEE Netw. 35(2), 74–81 (2021). https://doi.org/10.1109/MNET.011.2000326

    Article  Google Scholar 

  20. Nakamura, M., Wang, J., Phea, S., Abdallah, A.B.: Comprehensive study of coronavirus disease 2019 (COVID-19) classification based on deep convolution neural networks. In: SHS Web of Conferences, vol. 102, p. 04007. EDP Sciences (2021)

    Google Scholar 

  21. Ogbodo, M., Vu, T., Dang, K., Abdallah, A.: Light-weight spiking neuron processing core for large-scale 3D-NoC based spiking neural network processing systems. In: 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 133–139. Institute of Electrical and Electronics Engineers (IEEE) (2020). https://doi.org/10.1109/BigComp48618.2020.00-86

  22. WHO: Who coronavirus (COVID-19) dashboard – who coronavirus (COVID-19) dashboard with vaccination data. https://covid19.who.int/ (2022). Accessed on 05 Jun 2022

  23. Rahman, T., et al.: Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Comput. Biol. Med. 132, 104319 (2021)

    Google Scholar 

  24. Sorbaro, M., Liu, Q., Bortone, M., Sheik, S.: Optimizing the energy consumption of spiking neural networks for neuromorphic applications. Front. Neurosci. 14 (2020). https://doi.org/10.3389/fnins.2020.00662,

  25. Strubell, E., Ganesh, A., McCallum, A.: Energy and policy considerations for deep learning in NLP. Computing Research Repository abs/1906.02243 (2019). https://arxiv.org/abs/1906.02243

  26. Tang, S., et al.: EDL-COVID: ensemble deep learning for COVID-19 case detection from chest x-ray images. IEEE Trans. Industr. Inf. 17(9), 6539–6549 (2021)

    Article  Google Scholar 

  27. Waheed, A., Goyal, M., Gupta, D., Khanna, A., Al-Turjman, F., Pinheiro, P.R.: COVIDGAN: data augmentation using auxiliary classifier GAN for improved COVID-19 detection. IEEE Access 8, 91916–91923 (2020)

    Article  Google Scholar 

  28. Wang, J., et al.: Prior-attention residual learning for more discriminative COVID-19 screening in CT images. IEEE Trans. Med. Imaging 39(8), 2572–2583 (2020). https://doi.org/10.1109/TMI.2020.2994908

    Article  Google Scholar 

  29. Wang, J., Nakamura, M., Abdallah, A.B.: Efficient AI-enabled pneumonia detection in chest X-ray images. In: 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech), pp. 470–474 (2022). https://doi.org/10.1109/LifeTech53646.2022.9754850

  30. Wang, X., et al.: A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT. IEEE Trans. Med. Imaging 39(8), 2615–2625 (2020). https://doi.org/10.1109/TMI.2020.2995965

    Article  Google Scholar 

  31. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3462–3471 (2017). https://doi.org/10.1109/CVPR.2017.369

  32. Yuuki, O., Wang, J., Ikechukwu, O.M., Abdallah, A.B.: Hardware acceleration of convolution neural network for AI-enabled realtime biomedical system. In: Roy, D., Fragulis, G., Campos, H.C. (eds.) The 4th ETLTC International Conference on Information and Communications Technology, vol. 102, p. 04019. EDP Sciences (2021). https://doi.org/10.1051/shsconf/202110204019,

  33. Zhang, J., et al.: Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection. IEEE Trans. Med. Imaging 40(3), 879–890 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Adaptive Systems Laboratory, Graduate School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, 965-8580, Japan

    Tomohide Fukuchi, Mark Ikechukwu Ogbodo, Jiangkun Wang, Khanh N. Dang & Abderazek Ben Abdallah

Authors
  1. Tomohide Fukuchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark Ikechukwu Ogbodo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiangkun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Khanh N. Dang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abderazek Ben Abdallah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abderazek Ben Abdallah .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  3. University of Pau and Pays de l'Adour, Anglet, France

    Richard Chbeir

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Adrianna Kozierkiewicz

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fukuchi, T., Ogbodo, M.I., Wang, J., Dang, K.N., Ben Abdallah, A. (2022). Efficient Pneumonia Detection Method and Implementation in Chest X-ray Images Based on a Neuromorphic Spiking Neural Network. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2022. Lecture Notes in Computer Science(), vol 13501. Springer, Cham. https://doi.org/10.1007/978-3-031-16014-1_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-16014-1_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16013-4

  • Online ISBN: 978-3-031-16014-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation