Skip to main content
Springer Nature Link
Log in

COVID-ViT: COVID-19 Detection Method Based on Vision Transformers

  • Conference paper
  • First Online:
Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 716))

  • 390 Accesses

Abstract

The Coronavirus Disease 2019 (COVID-19) has had a devastating impact on healthcare systems, requiring improvements to the screening process of infected patients in Emergency Departments, being chest radiography a fundamental approach. This work, thoroughly documenting the development of this system, provides an overview of how cutting-edge technology such as Vision Transformers can be used for diagnosing COVID-19 by analyzing chest X-rays (CXR), including an explanation of how the network is fine-tuned and how it was validated. Through the COVID-Net Open Source Initiative, which provides a dataset of 30000 CXR images, our proposed Vision Transformer model obtains an accuracy of 94.75% and a sensitivity for COVID-19 cases of 99%, outperforming other widely used models in literature such as ResNet-50, VGG-19 or COVID-Net.

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. Johns Hopkins University. COVID-19 Map Dashboard. https://coronavirus.jhu.edu/map.html (2022). Accessed 25 Sept 2022

  2. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-End Object Detection with Transformers. (arXiv, 2020). https://arxiv.org/abs/2005.12872

  3. Chen, X., Xie, S., He, K.: An empirical study of training self-supervised vision transformers. (arXiv, 2021). https://arxiv.org/abs/2104.02057

  4. Wang, L., Lin, Z., Wong, A.: COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Scientific Rep. 10, 19549 (2020). https://doi.org/10.1038/s41598-020-76550-z

  5. Ding, W., Nayak, J., Swapnarekha, H., Abraham, A., Naik, B., Pelusi, D. Fusion of intelligent learning for COVID-19: a state-of-the-art review and analysis on real medical data. Neurocomputing 457, 40–66 (2021). https://doi.org/10.1016/j.neucom.2021.06.024

  6. Das, P., Nayak, B., Meher, S.: A lightweight deep learning system for automatic detection of blood cancer. Measurement 191 110762 (2022). https://www.sciencedirect.com/science/article/pii/S026322412200063X

  7. Das, P., Meher, S., Panda, R., Abraham, A.: A review of automated methods for the detection of sickle cell disease. IEEE Rev. Biomed. Eng. 13, 309–324 (2020)

    Article  Google Scholar 

  8. Das, P.K., Diya, V.A., Meher, S., Panda, R., Abraham, A.: A systematic review on recent advancements in deep and machine learning based detection and classification of acute lymphoblastic leukemia. IEEE Access 10 81741–81763 (2022)

    Google Scholar 

  9. Das, P., Meher, S.: Transfer learning-based automatic detection of acute lymphocytic leukemia. In: 2021 National Conference On Communications (NCC), pp. 1–6 (2021)

    Google Scholar 

  10. Das, P., Meher, S., Panda, R., Abraham, A.: An efficient blood-cell segmentation for the detection of hematological disorders. IEEE Trans. Cybern. 52, 10615–10626 (2022)

    Article  Google Scholar 

  11. Naik, M., Swain, M., Panda, R., Abraham, A.: Novel square error minimization-based multilevel thresholding method for COVID-19 X-ray image analysis using fast cuckoo search. International Journal of Image and Graphics (2022)

    Google Scholar 

  12. Goodarzian, F., Ghasemi, P., Gunasekaran, A., Taleizadeh, A., Abraham, A.: A sustainable-resilience healthcare network for handling COVID-19 pandemic. Annal. Oper. Res. 312, 1–65 (2022)

    Google Scholar 

  13. Rahaman, M., et al.: Identification of COVID-19 samples from chest X-Ray images using deep learning: a comparison of transfer learning approaches. J. X-ray Sci. Technol. 28, 821–839 (2020)

    Google Scholar 

  14. Cohen, J., Morrison, P., Dao, L., Roth, K., Duong, T., Ghassemi, M.: COVID-19 image data collection: prospective predictions are the future. (arXiv, 2020). https://arxiv.org/abs/2006.11988

  15. Pan, I., Cadrin-Chênevert, A., Cheng, P.: Tackling the radiological society of north America pneumonia detection challenge. Am. J. Roentgenol. 213, 568–574 (2019)

    Article  Google Scholar 

  16. Apostolopoulos, I., Aznaouridis, S., Tzani, B.: Extracting possibly representative covid-19 biomarkers from x-ray images with deep learning approach and image data related to pulmonary diseases (2020)

    Google Scholar 

  17. Malhotra, A., et al.: Multi-task driven explainable diagnosis of COVID-19 using chest X-ray images. Pattern Recogn. 122, 108243 (2022). https://www.sciencedirect.com/science/article/pii/S0031320321004246

  18. Ozturk, T., et al.: Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Medi. 121, 103792 (2020). https://www.sciencedirect.com/science/article/pii/S0010482520301621

  19. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.: 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)

    Google Scholar 

  20. Iraji, M., Feizi-Derakhshi, M., Tanha, J.: COVID-19 detection using deep convolutional neural networks and binary-differential-algorithm-based feature selection on X-ray images. (arXiv, 2021). https://arxiv.org/abs/2104.07279

  21. Kermany, D., Zhang, K., Goldbaum, M.: Labeled optical coherence tomography (OCT) and chest X-ray images for classification (2018)

    Google Scholar 

  22. Arias-Garzón, D., et al.: COVID-19 detection in X-ray images using convolutional neural networks. Mach. Learn. Appl. 6, 100138 (2021). https://www.sciencedirect.com/science/article/pii/S2666827021000694

  23. Vayá, M., et al.: BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients. (arXiv, 2020). https://arxiv.org/abs/2006.01174

  24. Mondal, A., Bhattacharjee, A., Singla, P., Prathosh, A.: xViTCOS: explainable vision transformer based COVID-19 screening using radiography. IEEE J. Transl. Eng. Health Med. 10, 1–10 (2022)

    Article  Google Scholar 

  25. Park, S., et al.: Vision transformer for COVID-19 CXR diagnosis using chest X-ray feature corpus. (arXiv, 2021). https://arxiv.org/abs/2103.07055

  26. Choi, Y., Song, E., Kim, Y., Song, T.: Analysis of high-risk infant births and their mortality: ten years’ data from Chonnam national university hospital. Chonnam Med. J. 47, pp. 31-38 (2011)

    Google Scholar 

  27. Chetoui, M., Akhloufi, M.: Explainable vision transformers and radiomics for COVID-19 detection in chest X-rays. J. Clin. Med. 11, 3013 (2022). https://www.mdpi.com/2077-0383/11/11/3013

  28. Society for imaging informatics in medicine (SIIM). SIIM-FISABIO-RSNA COVID-19 Detection.https://www.kaggle.com/c/siim-covid19-detection

  29. Vaswani, A., et al.: Attention is all you need. (arXiv, 2017). https://arxiv.org/abs/1706.03762

  30. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. (arXiv, 2021). https://arxiv.org/abs/2010.11929

  31. Jiang, X., Zhu, Y., Cai, G., et al.: MXT: a new variant of pyramid vision transformer for multi-label chest X-ray image classification. Cogn. Comput. 14, 1362–1377 (2022). https://doi.org/10.1007/s12559-022-10032-4

  32. Vision Transformers. Google Research. https://github.com/google-research/vision_transformer

  33. Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., Dosovitskiy, A.: Do vision transformers see like convolutional neural networks? (arXiv, 2021). https://arxiv.org/abs/2108.08810

  34. Kermany, D., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 1122–1131.e9 (2018). https://www.sciencedirect.com/science/article/pii/S0092867418301545

  35. Labeled optical coherence tomography (OCT) and Chest x-ray images for classification. Mendeley Data. https://data.mendeley.com/datasets/rscbjbr9sj/2

Download references

Acknowledgements

This work was supported by the ‘Artificial Intelligence for the diagnosis and prognosis of COVID-19’ project (CV20-29480), funded by the Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Junta de Andalucía, the FEDER funds and the ‘Deep processing of time series: Central Nervous System Brain diagnosis from perfusion of MRI Images’ project (PID2020-118224RB-I00), funded by the Spanish Ministry of Science and Innovation.

Author information

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, DiCITS, DaSCI, University of Granada, 18071, Granada, Spain

    Luis Balderas & José M. Benítez

  2. Department of Software Engineering, DiCITS, DaSCI, University of Granada, 18071, Granada, Spain

    Miguel Lastra

  3. Department of Radiology, Hospital Universitario Virgen de las Nieves, 18014, Granada, Spain

    Antonio J. Láinez-Ramos-Bossini

Authors
  1. Luis Balderas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miguel Lastra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonio J. Láinez-Ramos-Bossini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José M. Benítez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luis Balderas .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

Rights and permissions

Reprints and permissions

Copyright information

© 2023 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

Balderas, L., Lastra, M., Láinez-Ramos-Bossini, A.J., Benítez, J.M. (2023). COVID-ViT: COVID-19 Detection Method Based on Vision Transformers. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-031-35501-1_8

Download citation

Publish with us

Policies and ethics