Skip to main content
SpringerLink
Log in

Efficient detection of glaucoma using double tier deep convolutional neural network

  • Original Paper
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

Glaucoma is an ocular disease which causes the eyes’ optic nerves to suffer from irreversible blindness because of increased intraocular pressure. Early detection and glaucoma screening can prevent loss of vision. A common way to diagnose the progression of glaucoma is through examination by a special ophthalmologist of the dilated pupil of the eye. But this approach is difficult and takes a lot of time, so automation can solve the problem with the concept of double tier deep convolution neural networks. This network is well suited for resolving this type of problem, as it can deduce hierarchical data from the image that allows them to discern among glaucoma and non-glaucoma diagnostic patterns. It is composed with different layers. Every layer contains hidden layers like turbidity, max pooling, output and the fully connected layer. The retinal images are processed in the hidden layers, and the results obtained are combined and classified as normal or glaucomatous. The efficiency of the double tier deep convolutional neural network has been compared with different existing recognition methodologies. The outcomes show that the double tier deep convolutional neural network gives better performance when compared with other methodologies in terms of accuracy of 92.64%, sensitivity of 92.18%, specificity of 91.20% and precision of 90.76%.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

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

Similar content being viewed by others

Data availability

Available.

Code availability

Available.

References

  1. Maheshwari S, Kanhangad V, Pachori RB, Bhandary SV, Acharya UR (2019) Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques. Comput Biol Med 105:72–80

    Article  Google Scholar 

  2. Masood S, Sharif M, Raza M, Yasmin M, Iqbal M, YounusJaved M (2015) Glaucoma disease: a survey. Curr Med Imaging 11:272–283

    Article  Google Scholar 

  3. Acharya UR, Bhat S, Koh JE, Bhandary SV, Adeli H (2017) A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images. Comput Biol Med 88:72–83

    Article  Google Scholar 

  4. Shingleton BJ, Gamell LS, O’Donoghue MW, Baylus SL, King R (1999) Long-term changes in intraocular pressure after clear corneal phacoemulsification: normal patients versus glaucoma suspect and glaucoma patients. J Cataract Refract Surg 25:885–890

    Article  Google Scholar 

  5. Jamous KF, Kalloniatis M, Hennessy MP, Agar A, Hayen A, Zangerl B (2015) Clinical model assisting with the collaborative care of glaucoma patients and suspects. Clin Exp Ophthalmol 43:308–319

    Article  Google Scholar 

  6. T. Khalil, M. U. Akram, S. Khalid, S. H. Dar, N. Ali, A study to identify limitations of existing automated systems to detect glaucoma at initial and curable stage, Int. J. Imaging Syst. Technol.,8 (2021).

  7. Quigley HA, Broman AT (2006) The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol 90:262–267

    Article  Google Scholar 

  8. Costagliola C, Dell’Omo R, Romano MR, Rinaldi M, Zeppa L, Parmeggiani F (2009) Pharmacotherapy of intraocular pressure: part I. Parasympathomimetic, sympathomimetic and sympatholytics. Expert Opin Pharmacother 10:2663–2677

    Article  Google Scholar 

  9. A. A. Salam, M. U. Akram, K. Wazir, S. M. Anwar, M. Majid, Autonomous glaucoma detection from fundus image using cup to disc ratio and hybrid features, in ISSPIT.), IEEE, 2015, 370–374.

  10. Sarhan A, Rokne J, Alhajj R (2019) Glaucoma detection using image processing techniques: a literature review. Comput. Med. Imaging Graph 78:101657

    Article  Google Scholar 

  11. Diaz-Pinto A, Colomer A, Naranjo V, Morales S, Xu Y, Frangi AF (2019) Retinal image synthesis and semi-supervised learning for glaucoma assessment. IEEE Trans Med Imaging 38(9):2211–2218

    Article  Google Scholar 

  12. Bokhari F, Syedia T, Sharif M, Yasmin M, Fernandes SL (2018) Fundus image segmentation and feature extraction for the detection of glaucoma: a new approach. Curr Med Imaging Rev 14:77–87

    Article  Google Scholar 

  13. A. Agarwal, S. Gulia, S. Chaudhary, M. K. Dutta, R. Burget, K. Riha, Automatic glaucoma detection using adaptive threshold based technique in fundus image, in (TSP.), IEEE, 2015, 416–420.

  14. Diaz-Pinto A, Morales S, Naranjo V, K¨ohler T, Mossi JM, Navea A (2019) CNNs for automatic glaucoma assessment using fundus images: an extensive validation. Biomed Eng Online 18:29

    Article  Google Scholar 

  15. Sengupta S, Singh A, Leopold HA, Gulati T, Lakshminarayanan V (2020) Ophthalmic diagnosis using deep learning with fundus images–a critical review. Artif Intell Med 102:101758

    Article  Google Scholar 

  16. Shabbir A et al (2021) Detection of glaucoma using retinal fundus images: a comprehensive review. Math Biosci Eng 18(3):2033–2076

    Article  Google Scholar 

  17. Raghavendra U , Anjan Gudigar, Sulatha V. Bhandary,Tejaswi N Rao, “A two layer sparse autoencoder for glaucoma identification with fundus images”, Journal of Medical Systems, https://doi.org/10.1007/s10916-019-1427-x, July 2019.

  18. Wei Zhou, Shaojie Qiao, Yugen Yi, Nan Han, Yuqi Chen & Gang Lei, “Automatic optic disc detection using low-rank representation based semi-supervised extreme learning machine”, International Journal of Machine Learning and Cybernetics, 12 February 2019.

  19. Sopharak A, Dailey MN, Uyyanonvara B, Barman S, Williamson T (2010) Khine Thet Nwe and Yin Aye Moe, “Machine learning approach to automatic exudate detection in retinal images from diabetic patients “. J Mod Opt 57(2):20

    Article  MATH  Google Scholar 

  20. Xu P., Wan C., Cheng J., Niu D., Liu J. (2017) Optic disc detection via deep learning in fundus images. In: Cardoso M. et al. (eds) Fetal, infant and ophthalmic medical image analysis. OMIA 2017, FIFI 2017. Lecture notes in computer science, vol 10554. Springer, Cham.

  21. Seema Tukaram Kamble, S.A.Patil, “Automatic detection of optic disc using structural learning”, International Journal of Engineering Research & Technology (IJERT), ISSN: 2278–0181, IJERTV7IS050091, Vol. 7 Issue 05, May-2018.

  22. LeiWang HanLiu (2019) YalingLu, HangChen, JianZhang, JiantaoPu, “A coarse-to-fine deep learning framework for optic disc segmentation in fundus images.” Biomed SignalProcessing Control 51:82–89

    Article  Google Scholar 

  23. Shuang Yu , Di Xiao , Yogesan Kanagasingam, “Machine learning based automatic neovascularization detection on optic disc region”, IEEE Journal of Biomedical and Health Informatics , Volume: 22 , Issue: 3 , May 201.

  24. Sohini Roychowdhury, “Optic disc boundary and vessel origin segmentation of fundus images”, IEEE Journal of Biomedical and Health Informatics,2015.

  25. Joshi GD, Sivaswamy J, Krishnadas SR (2011) Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Trans Med Imag 30(6):1192–1205

    Article  Google Scholar 

  26. A.Padma, Dr.M.Sivajothi, Dr.M.Mohamed Sathik, A contemporary strategy for the recognition of glaucoma with tripartite tier convolutional neural network annals of R.S.C.B., ISSN:1583–6258, Vol. 25, Issue 5, 2021, Pages. 883–898

  27. Al-Bander B, Al-Nuaimy W, Williams BM, Zheng Y (2018) Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc, Biomed. Signal Process. Control 40:91–101

    Google Scholar 

  28. Murugesan K et al (2021) Color-based SAR image segmentation using HSV+FKM clustering for estimating the deforestation rate of LBA-ECO LC-14 modeled deforestation scenarios, Amazon basin: 2002–2050. Arab J Geosci 14(9):777

    Article  Google Scholar 

  29. Mugunthan SR, Vijayakumar T (2021) Design of improved version of sigmoidal function with biases for classification task in ELM domain. J Soft Comput Paradigm (JSCP) 3(02):70–82

    Article  Google Scholar 

  30. Manoharan S (2020) Early diagnosis of lung cancer with probability of malignancy calculation and automatic segmentation of lung CT scan images. J Innov Image Process (JIIP) 2(04):175–186

    Article  Google Scholar 

  31. Joon Tae et al (2021) TRk-CNN: Transferable ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes. Expert Syst Appl 182(15):115211

    Article  Google Scholar 

  32. H.N. Veena, A. Muruganandham, T. Senthil Kumaran. Enhanced CNN-RNN deep learning-based framework for the detection of glaucoma, International Journal of Biomedical Engineering and Technology , 36(2),2021

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Telangana, India

    Ch. Madhu Babu, G. Prabaharan & R. Pitchai

Authors
  1. Ch. Madhu Babu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Prabaharan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Pitchai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. Prabaharan.

Ethics declarations

Ethics approval

No human or animals used in our research.

Consent to participate

Not applicable.

Consent for publication

We give our consent for the publication of identifiable details, which can include figures and graphs within the text to be published in the Personal and Ubiquitous Computing journal.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Babu, C.M., Prabaharan, G. & Pitchai, R. Efficient detection of glaucoma using double tier deep convolutional neural network. Pers Ubiquit Comput 27, 1003–1013 (2023). https://doi.org/10.1007/s00779-022-01673-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-022-01673-1

Keywords

Search

Navigation