Skip to main content
SpringerLink
Log in

Automatic glaucoma detection from fundus images using transfer learning

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

Abstract

Glaucoma is an eye disease that damages the optic nerve (or retina) and impairs vision. This disease can be prevented with regular checkups, but this increases the workload for professionals and the time it takes to get results. So an automated method using deep learning would be helpful for detection of disease. In order to shorten the diagnosis time for glaucoma, this paper proposed a deep learning based method for automatic glaucoma detection. The experiments are conducted on glaucoma datasets available on Kaggle. This paper used transfer learning based pretrained models namely DenseNet169, MobileNet, InceptionV3, Xception, ReseNet152V2,and VGG19. Among all models DenseNet169 gives best result with accuracy 0.993590 and precision and recall of 0.993671 and 0.9935895 respectively. A comparison of the best model results with existing work shows that the proposed model provides better results.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data Availability

The Glaucoma [6] is openly available online on kaggle and can be accessed through the URL: https://www.kaggle.com/datasets/himanshuagarwal1998/glaucomadataset.

References

  1. Glaucoma (2023) https://www.nei.nih.gov/learn-about-eye-health/eye-conditions-anddiseases/glaucoma

  2. Vision impairment and blindness (2023) https://www.who.int/en/news-room/factsheets/detail/blindness-and-visual-impairment

  3. Abbas Q (2017) Glaucoma-deep: detection of glaucoma eye disease on retinal fundus images using deep learning. Int J Adv Comput Sci Appl 8(6). https://doi.org/10.14569/IJACSA.2017.080606

  4. Abdani SR, Zulkifley MA, Kamari NAM, Moubark AM (2022) Optimal selection of parallel atrous convolutions for mobilenet v3. In: Proceedings of the 11th international conference on robotics, vision, signal processing and power applications. Springer, pp 985–990. https://doi.org/10.1007/978-981-16-8129-5150

  5. Afroze T, Akther S, Chowdhury MA, Hossain E, Hossain MS, Andersson K (2021) Glaucoma detection using inception convolutional neural network v3. In: International conference on applied intelligence and informatics. Springer, pp 17–28. https://doi.org/10.1007/978-3-030-82269-9_2

  6. Agarwal H (2020) Glaucomadataset. https://www.kaggle.com/himanshuagarwal1998/glaucomadataset

  7. Ajesh F, Abraham A (2021) Detection and classification of age-related macular degeneration using integration of densenet169 and convolutional neural network. In: International conference on innovations in bio-inspired computing and applications. Springer, pp 226–238. https://doi.org/10.1007/978-3-030-96299-9_22

  8. Arora A, Gupta S, Singh S, Dubey J (2023) Eye disease detection using transfer learning on vgg16. In: Proceedings of third international conference on computing, communications, and cyber-security. Springer, pp 527–536. https://doi.org/10.1007/978-981-19-1142-2_42

  9. Atalay E, Özalp O, Devecioğlu ÖC, Erdoğan H, İnce T, Yıldırım N (2022) Investigation of the role of convolutional neural network architectures in the diagnosis of glaucoma using color fundus photography. Turkish J Ophthalmol 52(3):193. https://doi.org/10.4274/tjo.galenos.2021.29726

    Article  Google Scholar 

  10. Bansal M, Kumar M, Sachdeva M, Mittal A (2021) Transfer learning for image classification using vgg19: caltech-101 image data set. J Ambient Intell Humaniz Comput 1–12. https://doi.org/10.1007/s12652-021-03488-z

  11. Chen X, Xu Y, Wong DWK, Wong TY, Liu J (2015) Glaucoma detection based on deep convolutional neural network. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 715–718. https://doi.org/10.1109/EMBC.2015.7318462

  12. Cho H, Hwang YH, Chung JK, Lee KB, Park JS, Kim HG, Jeong JH (2021) Deep learning ensemble method for classifying glaucoma stages using fundus photographs and convolutional neural networks. Curr Eye Res 46(10):1516–1524. https://doi.org/10.1080/02713683.2021.1900268

    Article  CAS  PubMed  Google Scholar 

  13. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258. https://doi.ieeecomputersociety.org/10.1109/CVPR.2017.195

  14. Fang H, Li F, Fu H, Sun X, Cao X, Son J, Yu S, Zhang M, Yuan C, Bian C et al (2022) Refuge2 challenge: treasure for multi-domain learning in glaucoma assessment. arXiv:2202.08994

  15. Garg H, Gupta N, Agrawal R, Shivani S, Sharma B (2022) A real time cloud-based framework for glaucoma screening using efficientnet. Multimed Tools Appl 1–22. https://doi.org/10.1007/s11042-021-11559-8

  16. Haq DZ, Awwabi L, Hidayati SC, Herumurti D (2022) Glaucoma detection based-on convolution neural network and fundus image enhancement. In: 2022 10th international conference on information and communication technology (ICoICT). IEEE, pp 6–11. https://doi.org/10.1109/ICoICT55009.2022.9914849

  17. Hemelings R, Elen B, Barbosa-Breda J, Lemmens S, Meire M, Pourjavan S, Vandewalle E, Van de Veire S, Blaschko MB, De Boever P et al (2020) Accurate prediction of glaucoma from colour fundus images with a convolutional neural network that relies on active and transfer learning. Acta Ophthalmol 98(1):e94–e100. https://doi.org/10.1111/aos.14193

    Article  PubMed  Google Scholar 

  18. Hicks SA, Strümke I, Thambawita V, Hammou M, Riegler MA, Halvorsen P, Parasa S (2022) On evaluation metrics for medical applications of artificial intelligence. Sci Rep 12(1):1–9. https://doi.org/10.1101/2021.04.07.21254975

    Article  Google Scholar 

  19. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708. https://doi.ieeecomputersociety.org/10.1109/CVPR.2017.243

  20. Kumar Y, Gupta S (2022) Deep transfer learning approaches to predict glaucoma, cataract, choroidal neovascularization, diabetic macular edema, drusen and healthy eyes: an experimental review. Archives Comput Methods Eng 1–21. https://doi.org/10.1007/s11831-022-09807-7

  21. Li F, Chen H, Liu Z, Zhang X, Wu Z (2019) Fully automated detection of retinal disorders by image-based deep learning. Graefes Arch Clin Exp Ophthalmol 257(3):495–505. https://doi.org/10.1007/s00417-018-04224-8

    Article  PubMed  Google Scholar 

  22. Li N, Li T, Hu C, Wang K, Kang H (2021) A benchmark of ocular disease intelligent recognition: one shot for multi-disease detection. In: International symposium on benchmarking, measuring and optimization. Springer, pp 177–193. https://doi.org/10.1007/978-3-030-71058-3_11

  23. Maetschke S, Antony B, Ishikawa H, Wollstein G, Schuman J, Garnavi R (2019) A feature agnostic approach for glaucoma detection in oct volumes. PLoS ONE 14(7):e0219126. https://doi.org/10.1371/journal.pone.0219126

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mallick S, Paul J, Sengupta N, Sil J (2022) Study of different transformer based networks for glaucoma detection. In: TENCON 2022-2022 IEEE region 10 conference (TENCON). IEEE, pp 1–6. https://doi.org/10.1109/TENCON55691.2022.9977730

  25. Moses K, Miglani A, Kankar PK et al (2022) Deep cnn-based damage classification of milled rice grains using a high-magnification image dataset. Comput Electron Agric 195:106811. https://doi.org/10.1016/j.compag.2022.106811

    Article  Google Scholar 

  26. Nair V, Suranglikar S, Deshmukh S, Gavhane Y (2021) Multi-labelled ocular disease diagnosis enforcing transfer learning. In: 2021 55th annual conference on information sciences and systems (CISS). IEEE, pp 1–6. https://doi.org/10.1109/CISS50987.2021.9400227

  27. Nawaldgi S, YS L (2023) Automated glaucoma detection from fundus images using cnn models. https://doi.org/10.2139/ssrn.3991519

  28. Nayak SR, Nayak J, Sinha U, Arora V, Ghosh U, Satapathy SC (2021) An automated lightweight deep neural network for diagnosis of covid-19 from chest x-ray images. Arabian J Sci Eng 1–18. https://doi.org/10.1007/s13369-021-05956-2

  29. Ovreiu S, Paraschiv EA, Ovreiu E (2021) Deep learning & digital fundus images: glaucoma detection using densenet. In: 2021 13th international conference on electronics, computers and artificial intelligence (ECAI). IEEE, pp 1–4. https://doi.org/10.1109/ECAI52376.2021.9515188

  30. Paul A, Pramanik R, Malakar S, Sarkar R (2022) An ensemble of deep transfer learning models for handwritten music symbol recognition. Neural Comput Appl 34(13):10409–10427. https://doi.org/10.1007/s00521-021-06629-9

    Article  Google Scholar 

  31. Poh CY, Teoh SS (2022) Performance evaluation of optic disc detection using faster rcnn with alexnet, resnet50 and vgg19 convolutional neural networks. In: Proceedings of the 11th international conference on robotics, vision, signal processing and power applications. Springer, pp 753–758. https://doi.org/10.1007/978-981-16-8129-5_115

  32. Polat Ö (2021) Detection of covid-19 from chest ct images using xception architecture: a deep transfer learning based approach. Sakarya Univ J Sci 25(3):813–823. https://doi.org/10.16984/saufenbilder.903886

  33. Raghavendra U, Fujita H, Bhandary SV, Gudigar A, Tan JH, Acharya UR (2018) Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Inf Sci 441:41–49. https://doi.org/10.1016/j.ins.2018.01.051

    Article  MathSciNet  Google Scholar 

  34. Savaş S (2022) Detecting the stages of alzheimer’s disease with pre-trained deep learning architectures. Arab J Sci Eng 47(2):2201–2218. https://doi.org/10.1007/s13369-021-06131-3

  35. Serte S, Serener A (2019) A generalized deep learning model for glaucoma detection. In: 2019 3rd international symposium on multidisciplinary studies and innovative technologies (ISMSIT). IEEE, pp 1–5. https://doi.org/10.1109/ISMSIT.2019.8932753

  36. Shadin NS, Sanjana S, Chakraborty S, Sharmin N (2022) Performance analysis of glaucoma detection using deep learning models. In: 2022 international conference on innovations in science, engineering and technology (ICISET). IEEE, pp 190–195. https://doi.org/10.1109/ICISET54810.2022.9775828

  37. Sharmila C, Shanthi N (2021) Retinal image analysis for glaucoma detection using transfer learning. In: Advances in electrical and computer technologies. Springer, pp 235–244. https://doi.org/10.1007/978-981-15-9019-1 21

  38. Shyla N, Emmanuel W (2022) Glaucoma detection and classification using modified level set segmentation and pattern classification neural network. Multimed Tools Appl 1–19. https://doi.org/10.1007/s11042-022-13892-y

  39. Taj IA, Sajid M, Karimov KS et al (2021) An ensemble framework based on deep cnns architecture for glaucoma classification using fundus photography. Math Biosci Eng 18(5):5321–5347. https://doi.org/10.3934/mbe.2021270

    Article  MathSciNet  PubMed  Google Scholar 

  40. Ubaidah IDS, Fu’Adah Y, Sa’Idah S, Magdalena R, Wiratama AB, Simanjuntak RBJ (2022) Classification of glaucoma in fundus images using convolutional neural network with mobilenet architecture. In: 2022 1st international conference on information system & information technology (ICISIT). IEEE, pp 198–203. https://doi.org/10.1109/ICISIT54091.2022.9872945

  41. Visa S, Ramsay B, Ralescu AL, Van Der Knaap E (2011) Confusion matrix-based feature selection. MAICS 710:120–127. https://ceur-ws.org/Vol-710/paper37.pdf

  42. Vrbačič G, Pečnik Š, Podgorelec V (2022) Hyper-parameter optimization of convolutional neural networks for classifying covid-19 x-ray images. Comput Sci Inf Syst 19(1):327–352. https://doi.org/10.2298/CSIS210209056V

    Article  Google Scholar 

  43. Yakut C, Oksuz I, Ulukaya S (2022) A hybrid fusion method combining spatial image filtering with parallel channel network for retinal vessel segmentation. Arabian J Sci Eng 1–14. https://doi.org/10.1007/s13369-022-07311-5

Download references

Funding

No funding was received to assist with the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Indian Institute of Information Technology, Pune, India

    Rajeshwar Patil & Sanjeev Sharma

Authors
  1. Rajeshwar Patil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanjeev Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rajeshwar Patil.

Ethics declarations

Conflicts of interest

The authors declare that they have no confict of interest.

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

Patil, R., Sharma, S. Automatic glaucoma detection from fundus images using transfer learning. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18242-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-18242-8

Keywords

Search

Navigation