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Near Real-Time Federated Machine Learning Approach Over Chest Computed Tomography for COVID-19 Diagnosis

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Applications and Techniques in Information Security (ATIS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1554))

Abstract

During the COVID-19 pandemic, artificial intelligence (AI) plays a major role to detect and distinguish between several lungs diseases and diagnose COVID-19 cases accurately. This article studies the feasibility of the federated learning (FL) approach for identifying and distinguishing COVID-19 X-ray images. We trained and tested FL components by using the data sets that collect images of three different lungs conditions, COVID-19, common lungs and viral pneumonia. We develop and evaluate FL model horizontally with same parameters and compare the performance with the classic CNN model and the transfer learning approaches. We found that FL can quickly train artificial intelligence models on different devices during a pandemic, avoiding privacy leaks that may be caused by such a high resolution personal and private X-ray data.

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References

  1. Abiyev, R.H., Ma’aitah, M.K.S.: Deep convolutional neural networks for chest diseases detection. J. Healthcare Eng. 2018 (2018)

    Google Scholar 

  2. Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), pp. 1–6. IEEE (2017)

    Google Scholar 

  3. Arena, P., Basile, A., Bucolo, M., Fortuna, L.: Image processing for medical diagnosis using CNN. Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrom. Detect. Assoc. Equip. 497(1), 174–178 (2003)

    Article  Google Scholar 

  4. Bernheim, A., et al.: Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology 295, 200463 (2020)

    Article  Google Scholar 

  5. Brinati, D., Campagner, A., Ferrari, D., Locatelli, M., Banfi, G., Cabitza, F.: Detection of COVID-19 infection from routine blood exams with machine learning: a feasibility study. J. Med. Syst. 44(8), 1–12 (2020)

    Article  Google Scholar 

  6. Cellina, M., Orsi, M., Toluian, T., Pittino, C.V., Oliva, G.: False negative chest X-rays in patients affected by COVID-19 pneumonia and corresponding chest CT findings. Radiography 26(3), e189–e194 (2020)

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Cleverley, J., Piper, J., Jones, M.M.: The role of chest radiography in confirming COVID-19 pneumonia. BMJ 370, m2426 (2020)

    Article  Google Scholar 

  9. Das, N.N., Kumar, N., Kaur, M., Kumar, V., Singh, D.: Automated deep transfer learning-based approach for detection of COVID-19 infection in chest x-rays. IRBM (2020)

    Google Scholar 

  10. Ghaderzadeh, M., Asadi, F.: Deep learning in the detection and diagnosis of COVID-19 using radiology modalities: a systematic review. J. Healthcare Eng. 2021 (2021)

    Google Scholar 

  11. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  12. Ismael, A.M., Şengür, A.: Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Syst. Appl. 164, 114054 (2021)

    Article  Google Scholar 

  13. Kanne, J.P., Little, B.P., Chung, J.H., Elicker, B.M., Ketai, L.H.: Essentials for radiologists on COVID-19: an update-radiology scientific expert panel. Radiology 296, E113–E114 (2020)

    Article  Google Scholar 

  14. Kayaalp, M.: Patient privacy in the era of big data. Balkan Med. J. 35(1), 8 (2018)

    Article  Google Scholar 

  15. Khalifa, N.E.M., Taha, M.H.N., Hassanien, A.E., Taha, S.H.N.: The detection of COVID-19 in CT medical images: a deep learning approach. In: Hassanien, A.-E., Dey, N., Elghamrawy, S. (eds.) Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach. SBD, vol. 78, pp. 73–90. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-55258-9_5

    Chapter  Google Scholar 

  16. Khan, A., Sohail, A., Zahoora, U., Qureshi, A.S.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53(8), 5455–5516 (2020). https://doi.org/10.1007/s10462-020-09825-6

    Article  Google Scholar 

  17. Khuzani, A.Z., Heidari, M., Shariati, S.A.: COVID-classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images. Sci. Rep. 11(1), 1–6 (2021)

    Article  Google Scholar 

  18. McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282. PMLR (2017)

    Google Scholar 

  19. Narin, A.: Accurate detection of COVID-19 using deep features based on x-ray images and feature selection methods. Comput. Biol. Med. 137, 104771 (2021)

    Article  Google Scholar 

  20. Nasr, M., Shokri, R., Houmansadr, A.: Comprehensive privacy analysis of deep learning: passive and active white-box inference attacks against centralized and federated learning. In: 2019 IEEE Symposium on Security and Privacy (SP), pp. 739–753. IEEE (2019)

    Google Scholar 

  21. de Oliveira Andrade, R.: COVID-19 is causing the collapse of Brazil’s national health service. BMJ 370, m3032 (2020)

    Article  Google Scholar 

  22. Panwar, H., Gupta, P., Siddiqui, M.K., Morales-Menendez, R., Singh, V.: Application of deep learning for fast detection of COVID-19 in X-rays using nCOVnet. Chaos Solitons Fractals 138, 109944 (2020)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  24. Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights Imaging 9(4), 611–629 (2018). https://doi.org/10.1007/s13244-018-0639-9

    Article  Google Scholar 

  25. Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1–19 (2019)

    Article  Google Scholar 

  26. Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., Gao, Y.: A survey on federated learning. Knowl.-Based Syst. 216, 106775 (2021)

    Article  Google Scholar 

  27. Zhang, L., Xiang, F.: Relation classification via BiLSTM-CNN. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 373–382. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_35

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Authors and Affiliations

  1. School of IT, Deakin University, Geelong, VIC, 3216, Australia

    Yang Cao

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  1. Yang Cao
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Correspondence to Yang Cao .

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Editors and Affiliations

  1. Deakin University, Burwood, Australia

    Shiva Raj Pokhrel

  2. Chinese Academy of Sciences, Beijing, China

    Min Yu

  3. Deakin University, Burwood, Australia

    Gang Li

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Cao, Y. (2022). Near Real-Time Federated Machine Learning Approach Over Chest Computed Tomography for COVID-19 Diagnosis. In: Pokhrel, S.R., Yu, M., Li, G. (eds) Applications and Techniques in Information Security. ATIS 2021. Communications in Computer and Information Science, vol 1554. Springer, Singapore. https://doi.org/10.1007/978-981-19-1166-8_3

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  • DOI: https://doi.org/10.1007/978-981-19-1166-8_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1165-1

  • Online ISBN: 978-981-19-1166-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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