Skip to main content
SpringerLink
Log in

Convolution Neural Network Based Approach for Glaucoma Disease Detection

  • Conference paper
  • First Online:
Artificial Intelligence: Theories and Applications (ICAITA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1769))

  • 283 Accesses

Abstract

Glaucoma is among the discreet and incurable eye diseases; it causes a slow and progressive decay of the retina of human eyes. The absence of clear symptoms during the early stages makes it hard to detect. This paper is dedicated to design a convolutional neural network (CNN) based approach to detect and diagnose glaucoma based on the processed funds images. It is divided into two phases: Learning and Classification. Results are promising to detect such a disease from retina images even when compared to other architectures.

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 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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. Hollows, F.C., Graham, P.A.: Intra-ocular pressure, glaucoma, and glaucoma suspects in a denned population. Br. J. Ophthalmol. 50(5954089), 570–586 (1966)

    Article  Google Scholar 

  2. Rafæl, R.E.W., Gonzalez, C.: Digital Image Processing (2008)

    Google Scholar 

  3. Leibowitz, H.M., et al.: The Framingham eye study monograph: an ophthalmological and epidemiological study of cataract, glaucoma, diabetic retinopathy, macular degeneration, and visual acuity. J. Surv. Ophthalmol. 24, 335–610 (1980)

    Google Scholar 

  4. Spalton, D.J., Hitchings, R.A., Hunter, P.: Atlas of Clinical Ophthalmology with CD-ROM. Elsevier, Amsterdam (2013)

    Google Scholar 

  5. Zhang, F., et al.: An online retinal fundus image database for glaucoma analysis and research. In: Proc. Int. Conf. IEEE Eng. Med. Biol., pp. 3065–3068 (2010)

    Google Scholar 

  6. Ciresan, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 2, pp. 1237–1242 (2013)

    Google Scholar 

  7. Aghdam, H.H., Heravi, E.J.: Guide to Convolutional Neural Networks. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-57550-6

    Book  Google Scholar 

  8. Balas, V.E., Kumar, R., Srivastava, R.: Recent Trends and Advances in Artificial Intelligence and Internet of Things. Springer Nature (2019)

    Google Scholar 

  9. Fuente-Arriaga, E., Felipe-Riverón, M., Garduño-Calderón, E.: Application of vascular bundle displacement in the optic disc for glaucoma detection using fundus images. J. Comput. Biol. Med. 47, 222–235 (2014)

    Article  Google Scholar 

  10. Ciresan, D., Meier, U., Masci, J.: Flexible, high performance convolutional neural networks for image classification. Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16–22 (2011)

    Google Scholar 

  11. Azulay, A., Weiss, Y.: Why do deep convolutional networks generalize so poorly to small image transformations. J. Mach. Learn. Res. 20(184) (2019)

    Google Scholar 

  12. LeCun, Y.: Convolutional Neural Networks (2013)

    Google Scholar 

  13. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386

    Article  Google Scholar 

  14. https://www.journaldunet.fr/web-tech/guide-de-l-intelligenceartificielle/1501309apprentissage-non-supervise/. 10 Apr 2022

  15. https://intelligence-artificielle.com/classification-d-image-guide-complet. 5 Apr 2022

  16. Pooja, K., et al.: A survey on image classification approaches and techniques. Int. J. Adv. Res. Comput. Commun. Eng. 2 (2013)

    Google Scholar 

  17. Chen, X., Xu, Y., Wong, D.W.K., Wong, T.Y., Liu, J.: Glaucoma detection based on deep convolutional neural network. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 715–718 (2015)

    Google Scholar 

  18. World Health Organization. Bulletin of the World Health Organization. http://www.who.int/bulletin/. Accessed 5 May 2016

  19. Bock, R., Meier, J., Nyúl, L.G., Hornegger, J., Michelson, G.: Glaucoma risk index: automated glaucoma detection from color fundus images. Med. Image Anal. 14(3), 471–481 (2010). https://doi.org/10.1016/j.media.2009.12.006

    Article  Google Scholar 

  20. Khambra, G., Shukla, P.: Novel machine learning applications on fly ash based concrete: an overview. Mater. Today Proc. 7, 2214–7853 (2021)

    Google Scholar 

  21. Shubham, J., et al.: Glaucoma detection using image processing and supervised learning for classification. J. Healthc. Eng. 7, 1–12 (2022)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Mascara, Mascara, Algeria

    Sahraoui Mustapha, Salem Mohammed & Smail Oussama

Authors
  1. Sahraoui Mustapha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Salem Mohammed
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Smail Oussama
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sahraoui Mustapha .

Editor information

Editors and Affiliations

  1. University of Mascara, Mascara, Algeria

    Mohammed Salem

  2. University of Granada, Granada, Spain

    Juan Julián Merelo

  3. Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  4. University of Mascara, Mascara, Algeria

    Rochdi Bachir Bouiadjra

  5. University of Mascara, Mascara, Algeria

    Mohamed Debakla

  6. University of Mascara, Mascara, Algeria

    Fatima Debbat

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

Mustapha, S., Mohammed, S., Oussama, S. (2023). Convolution Neural Network Based Approach for Glaucoma Disease Detection. In: Salem, M., Merelo, J.J., Siarry, P., Bachir Bouiadjra, R., Debakla, M., Debbat, F. (eds) Artificial Intelligence: Theories and Applications. ICAITA 2022. Communications in Computer and Information Science, vol 1769. Springer, Cham. https://doi.org/10.1007/978-3-031-28540-0_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-28540-0_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28539-4

  • Online ISBN: 978-3-031-28540-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation