[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Automated detection of COVID-19 based on transfer learning

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The world has recently experienced unprecedented disruptions due to the COVID-19 pandemic, which have greatly impacted daily life. Deep Learning (DL) is a branch of Artificial Intelligence (AI) that has seen significant growth in recent years, and its features could be useful in the fight against COVID-19. By leveraging these features, public health efforts could be better supported. In this research, we propose a method for detecting COVID-19 positive patients using chest X-ray images. Our method employs pre-trained deep neural networks, specifically the DenseNet-169 and ResNet-50 architectures. For each architecture, we kept the basic model and replaced the border layers with Dense layers. We used 2Dense in the first iteration and 5Dense in the second iteration. Our results show that Transfer Learning (TL) is a useful technique for detecting COVID-19 cases. The DenseNet-169 + 2Dense, DenseNet-169 + 5Dense, and using the ELU function achieved the highest accuracy value of 90.04%.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The dataset used in this study is public and can be found at the following links:

https://drive.google.com/drive/folders/1NLjyns6qJcQE8zZ8OZ-xfNMatcLLnMJO

References

  1. Zhao S, Lin Q, Ran J, Musa SS, Yang G, Wang W, Lou Y, Gao D, Yang L, He D et al (2020) Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. Int J Infect Dis 92:214–217

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Rothan HA, Byrareddy SN (2020) The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J Autoimmun 109:102433

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y et al (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet 395:507–513

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Li Q, Guan X, Wu P et al (2020) Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med 382(13):1199–1207

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Ng M-Y, Lee EYP, Yang J et al (2020) Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiol: Cardiothoracic Imaging 2(1):e200034

    PubMed  PubMed Central  Google Scholar 

  6. Rubin GD, Ryerson CJ, Haramati LB et al (2020) The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner society. Chest 58(1):106–116

    Article  Google Scholar 

  7. Shan F, Gao Y, Wang J, Shi W, Shi N, Han M, Xue Z, Shen D, Shi Y (2020) Lung infection quantification of COVID-19 in CT images with deep learning. arXiv preprint arXiv:2003.04655

  8. Chouat I, Echtioui A, Khemakhem R, Zouch W, Ghorbel M, Kammoun I, Ben Hamida A (2022) Lung Disease Detection in Chest X-ray Images Using Transfer Learning, 6th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2022, 1–6, https://doi.org/10.1109/ATSIP55956.2022.9805892

  9. Chouat I, Echtioui A, Khemakhem R, Zouch W, Ghorbel M, Kammoun I, Ben Hamida A (2022) COVID-19 Detection in CT Images Using Deep Learning Models. Biogerontology 23:65–84

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zouch W, Sagga D, Echtioui A, Khemakhem R, Ghorbel M, Mhiri C, Ben Hamida A (2022) Detection of COVID-19 from CT and Chest X-ray Images Using Deep Learning Models. Ann Biomed Eng 50:825–835

    Article  PubMed  PubMed Central  Google Scholar 

  11. Echtioui A, Zouch W, Ghorbel M, Mhiri C, Hamam H (2020) Detection Methods of Coronavirus Disease (COVID-19). SLAS Technology: Trans Life Sciences Innov 25(6):566–572

    Article  CAS  Google Scholar 

  12. Apostolopoulos ID, Mpesiana TA (2020) COVID-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 43(2):635–640

    Article  PubMed  PubMed Central  Google Scholar 

  13. Canayaz M (2020) MH-COVIDNet: diagnosis of COVID-19 using deep neural networks and meta-heuristic-based feature selection on X-ray images. Biomed Signal Process Contr 64:102257

    Article  Google Scholar 

  14. Ahuja S, Panigrahi BK, Dey N, Rajinikanth V, Gandhi TK (2020) Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Appl Intell 51:571–585

    Article  Google Scholar 

  15. Ghaderzadeh M, Asadi F (2021) Deep learning in the detection and diagnosis of COVID-19 using radiology modalities: a systematic review. J Healthcare Eng 2021:6677314. https://doi.org/10.1155/2021/6677314

    Article  Google Scholar 

  16. Farooq M, Hafeez A (2020) Covid-resnet A deep learning framework for screening of COVID-19 from radiographs. arXiv 2020, arXiv:2003.14395

  17. Gupta A, Gupta S, Katarya R (2021) Instacovnet-19: A deep learning classification model for the detection of covid-19 patients using chest x-ray. Appl Soft Comput 99:106859

    Article  PubMed  Google Scholar 

  18. Ozturk T, Talo M, Yildirimc E, Baloglu U, Yildirim O, Acharya U (2020) Automated detection of covid-19 cases using deep neural networks with x-ray images. Comput Biol Med

  19. Jangam E, Barreto AAD, Annavarapu CSR (2022) Automatic detection of covid-19 from chest ct scan and chest x-rays images using deep learning, transfer learning and stacking. Appl Intell 52:2243–2259. https://doi.org/10.1007/s10489-021-02393-4

    Article  Google Scholar 

  20. Jangam E, Annavarapu CSR (2021) A stacked ensemble for the detection of covid-19 with high recall and accuracy. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2021.104608

    Article  PubMed  PubMed Central  Google Scholar 

  21. Wang L, Lin ZQ, Wong A (2020) Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Sci Rep 10(1):1–12

    CAS  Google Scholar 

  22. Khan AI, Shah JL, Bhat MM (2020) Coronet: a deep neural network for detection and diagnosis of covid-19 from chest x-ray images. Comput Methods Programs Biomed 196:105581

    Article  PubMed  PubMed Central  Google Scholar 

  23. Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR (2020) Automated detection of covid-19 cases using deep neural networks with x-ray images. Comput Biol Med 121:103792

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mahmud T, Rahman MA, Fattah SA (2020) Covxnet: a multi-dilation convolutional neural network for automatic covid-19 and other pneumonia detection from chest x-ray images with transferable multi-receptive feature optimization. Comput Biol Med 122:103869

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Loey M, Smarandache F, Khalifa MNE (2020) Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning. Symmetry 12:651

    Article  ADS  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Advanced Technologies for Medicine and Signals Laboratory ‘ATMS’, National Engineering School of Sfax (ENIS), Sfax University, Sfax, Tunisia

    Amira Echtioui

  2. MIRACL: Multimedia InfoRmation System and Advanced Computing Laboratory, National Engineering School of Sfax (ENIS), Sfax University, Sfax, Tunisia

    Yassine Ben Ayed

Authors
  1. Amira Echtioui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yassine Ben Ayed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amira Echtioui.

Ethics declarations

Consent to participate

This article does not contain any studies with animals or humans performed by any of the authors.

Conflict of interest

No conflict of interest to report.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Echtioui, A., Ayed, Y.B. Automated detection of COVID-19 based on transfer learning. Multimed Tools Appl 83, 33731–33751 (2024). https://doi.org/10.1007/s11042-023-17023-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17023-z

Keywords

Search

Navigation