SpringerLink
Log in

Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification

  • Research Article
  • Published:
Machine Intelligence Research Aims and scope Submit manuscript

Abstract

Glaucoma is a prevalent cause of blindness worldwide. If not treated promptly, it can cause vision and quality of life to deteriorate. According to statistics, glaucoma affects approximately 65 million individuals globally. Fundus image segmentation depends on the optic disc (OD) and optic cup (OC). This paper proposes a computational model to segment and classify retinal fundus images for glaucoma detection. Different data augmentation techniques were applied to prevent overfitting while employing several data pre-processing approaches to improve the image quality and achieve high accuracy. The segmentation models are based on an attention U-Net with three separate convolutional neural networks (CNNs) backbones: Inception-v3, visual geometry group 19 (VGG19), and residual neural network 50 (ResNet50). The classification models also employ a modified version of the above three CNN architectures. Using the RIM-ONE dataset, the attention U-Net with the ResNet50 model as the encoder backbone, achieved the best accuracy of 99.58% in segmenting OD. The Inception-v3 model had the highest accuracy of 98.79% for glaucoma classification among the evaluated segmentation, followed by the modified classification architectures.

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
EUR 32.99 /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 (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. World Health Organization. World report on vision, [Online], Available: https://apps.who.int/iris/bitstream/handle/10665/328717/9789241516570-eng.pdf?sequence=18&isAllowd=y, 2019.

  2. L. Storgaard, T. L. Tran, J. C. Freiberg, A. S. Hauser, M. Kolko. Glaucoma clinical research: Trends in treatment strategies and drug development. Frontiers in Medicine, vol. 8, Article number 733080, 2021. DOI: https://doi.org/10.3389/fmed.2021.733080.

  3. I. B. Senkyire, Z. Liu. Supervised and semi-supervised methods for abdominal organ segmentation: A review. International Journal of Automation and Computing, vol. 18, no. 6, pp. 887–914, 2021. DOI: https://doi.org/10.1007/s11633-021-1313-0.

    Article  Google Scholar 

  4. I. Rubasinghe, D. Meedeniya. Ultrasound nerve segmentation using deep probabilistic programming. Journal of ICT Research and Applications, vol. 13, no. 3, pp. 241–256, 2019. DOI: https://doi.org/10.5614/itbj.ict.res.appl.2019.13.3.5.

    Article  Google Scholar 

  5. Y. Prajna, M. K. Nath. Efficient blood vessel segmentation from color fundus image using deep neural network. Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3477–3489, 2022. DOI: https://doi.org/10.3233/jifs-211479.

    Article  Google Scholar 

  6. J. Kim, L. Tran, E. Y. Chew, S. Antani. Optic disc and cup segmentation for glaucoma characterization using deep learning. In Proceedings of the 32nd International Symposium on Computer-Based Medical Systems, IEEE, Córdoba, Spain, pp. 489–494, 2019. DOI: https://doi.org/10.1109/cbms.2019.00100.

    Google Scholar 

  7. R. Mahum, S. Ur Rehman, O. D. Okon, A. Alabrah, T. Meraj, H. T. Rauf. A novel hybrid approach based on deep CNN to detect glaucoma using fundus imaging. Electronics, vol. 11, no. 1, Article number 26, 2022. DOI: https://doi.org/10.3390/electronics11010026.

  8. G. Ariyarathne, S. De Silva, S. Dayarathna, D. Meedeniya, S. Jayarathne. ADHD identification using convolutional neural network with seed-based approach for fMRI data. In Proceedings of the 9th International Conference on Software and Computer Applications, ACM, Langkawi, Malaysia, pp. 31–35, 2020. DOI: https://doi.org/10.1145/3384544.3384552.

    Google Scholar 

  9. C. Z. Wu, J. Sun, J. Wang, L. F. Xu, S. Zhan. Encoding-decoding network with pyramid self-attention module for retinal vessel segmentation. International Journal of Automation and Computing, vol. 18, no. 6, pp. 973–980, 2021. DOI: https://doi.org/10.1007/s11633-020-1277-0.

    Article  Google Scholar 

  10. B. B. Jia, M. L. Zhang. Multi-dimensional classification via selective feature augmentation. Machine Intelligence Research, vol. 19, no. 1, pp. 38–51, 2022. DOI: https://doi.org/10.0007/s11633-022-1316-5.

    Article  Google Scholar 

  11. P. Elangovan, D. Vijayalakshmi, M. K. Nath. Detection of glaucoma from fundus image using pre-trained Densenet201 model. Indian Journal of Radio & Space Physics, vol. 50, no. 1, pp. 33–39, 2021.

    Google Scholar 

  12. T. Shyamalee, D. Meedeniya. CNN based fundus images classification for glaucoma identification. In Proceedings of the 2nd International Conference on Advanced Research in Computing, IEEE, Belihuloya, Sri Lanka, pp. 200–205, 2022. DOI: https://doi.org/10.1109/ICARC54489.2022.9754171.

    Google Scholar 

  13. P. Elangovan, M. K. Nath. Glaucoma assessment from color fundus images using convolutional neural network. International Journal of Imaging Systems and Technology, vol. 31, no. 2, pp. 955–971, 2021. DOI: https://doi.org/10.1002/ima.22494.

    Article  Google Scholar 

  14. M. B. Sudhan, M. Sinthuja, S. Pravinth Raja, J. Amutharaj, G. Charlyn Pushpa Latha, S. Sheeba Rachel, T. Anitha, T. Rajendran, Y. Asrat Waji. Segmentation and classification of glaucoma using U-net with deep learning model. Journal of Healthcare Engineering, vol. 2022, Article number 1601354, 2022. DOI: https://doi.org/10.1155/2022/1601354.

  15. O. Ronneberger, P. Fischer, T. Brox. U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of the 18th International Conference on Medical image Computing and Computer-assisted Intervention, Springer, Munich, Germany, pp. 234–241, 2015. DOI: https://doi.org/10.1007/978-3-319-24574-4_28.

    Google Scholar 

  16. X. Y. Zhao, S. Y. Wang, J. Zhao, H. C. Wei, M. X. **ao, N. Ta. Application of an attention U-Net incorporating transfer learning for optic disc and cup segmentation. Signal, Image and Video Processing, vol. 15, no. 5, pp. 913–921, 2021. DOI: https://doi.org/10.1007/s11760-020-01815-z.

    Article  Google Scholar 

  17. T. Shyamalee, D. Meedeniya. Attention U-Net for glaucoma identification using fundus image segmentation. In Proceedings of International Conference on Decision Aid Sciences and Applications, IEEE, Chiangrai, Thailand, pp. 6–10, 2022. DOI: https://doi.org/10.1109/dasa54658.2022.9765303.

    Google Scholar 

  18. L. Herath, D. Meedeniya, M. A. J. C. Marasingha, V. Weerasinghe. Autism spectrum disorder diagnosis support model using Inception-v3. In International Research Conference on Smart Computing and Systems Engineering, IEEE, Colombo, Sri Lanka, pp. 1–7, 2021. DOI: https://doi.org/10.1109/SCSE53661.2021.9568314.

    Google Scholar 

  19. C. Fernando, S. Kolonne, H. Kumarasinghe, D. Meedeniya. Chest radiographs classification using multi-model deep learning: A comparative study. In Proceedings of the 2nd International Conference on Advanced Research in Computing, IEEE, Belihuloya, Sri Lanka, pp. 165–170, 2022. DOI: https://doi.org/10.1109/ICARC54489.2022.9753811.

    Google Scholar 

  20. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna. Rethinking the inception architecture for computer vision. In Proceedings of Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 2818–2826, 2016. DOI: https://doi.org/10.1109/cvpr.2016.308.

  21. K. Simonyan, A. Zisserman. Very deep convolutional networks for large-scale image recognition. In Proceedings of the 3rd International Conference on Learning Representations, San Diego, USA, 2015.

  22. K. M. He, X. Y. Zhang, S. Q. Ren, J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 770–778, 2016. DOI: https://doi.org/10.1109/CVPR.2016.90.

  23. T. Iqbal, H. Ali. Generative adversarial network for medical images (MI-GAN). Journal of Medical Systems, vol. 42, no. 11, Article number 231, 2018. DOI: https://doi.org/10.1007/s10916-018-1072-9.

  24. S. J. Wang, L. Q. Yu, X. Yang, C. W. Fu, P. A. Heng. Patch-based output space adversarial learning for joint optic disc and cup segmentation. IEEE Transactions on Medical Imaging, vol. 38, no. 11, pp. 2485–2495, 2019. DOI: https://doi.org/10.1109/tmi.2019.2899910.

    Article  Google Scholar 

  25. M. Z. Alom, C. Yakopcic, M. Hasan, T. M. Taha, V. K. Asari. Recurrent residual U-Net for medical image segmentation. Journal of Medical Imaging, vol. 6, no. 1, Article number 014006, 2019. DOI: https://doi.org/10.1117/1.jmi.6.1.014006.

  26. A. You, J. K. Kim, I. H. Ryu, T. K. Yoo. Application of generative adversarial networks (GAN) for ophthalmology image domains: A survey. Eye and Vision, vol. 9, no. 1, Article number 6, 2022. DOI: https://doi.org/10.1186/s40662-022-00277-3.

  27. G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, C. I. Sánchez. A survey on deep learning in medical image analysis. Medical Image Analysis, vol. 42, pp. 60–88, 2017. DOI: https://doi.org/10.1016/j.media.2017.07.005.

    Article  Google Scholar 

  28. H. Z. Fu, J. Cheng, Y. W. Xu, D. W. K. Wong, J. Liu, X. C. Cao. Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Transactions on Medical Imaging, vol. 37, no. 7, pp. 1597–1605, 2018. DOI: https://doi.org/10.1109/tmi.2018.2791488.

    Article  Google Scholar 

  29. M. Tabassum, T. M. Khan, M. Arsalan, S. S. Naqvi, M. Ahmed, H. A. Madni, J. Mirza. CDED-Net: Joint segmentation of optic disc and optic cup for glaucoma screening. IEEE Access, vol. 8, pp. 102733–102747, 2020. DOI: https://doi.org/10.1109/access.2020.2998635.

    Article  Google Scholar 

  30. B. X. **, P. P. Liu, P. Wang, L. D. Shi, J. Zhao. Optic disc segmentation using attention-based U-Net and the improved cross-entropy convolutional neural network. Entropy, vol. 22, no. 8, Article number 844, 2020. DOI: https://doi.org/10.3390/e22080844.

  31. J. Civit-Masot, F. Luna-Perejón, S. Vicente-Díaz, J. M. R. Corral, A. Civit. TPU cloud-based generalized U-Net for eye fundus image segmentation. IEEE Access, vol. 7, pp. 142379–142387, 2019. DOI: https://doi.org/10.1109/access.2019.2944692.

    Article  Google Scholar 

  32. A. Sevastopolsky. Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network. Pattern Recognition and Image Analysis, vol. 27, no. 3, pp. 618–624, 2017. DOI: https://doi.org/10.1134/s1054661817030269.

    Article  Google Scholar 

  33. S. De Silva, S. U. Dayarathna, G. Ariyarathne, D. Meedeniya, S. Jayarathna. fMRI feature extraction model for ADHD classification using convolutional neural network. International Journal of E-Health and Medical Communications, vol. 12, no. 1, Article number 6, 2021. DOI: https://doi.org/10.4018/IJEHMC.2021010106.

  34. D. Haputhanthri, G. Brihadiswaran, S. Gunathilaka, D. Meedeniya, S. Jayarathna, M. Jaime, C. Harshaw. Integration of facial thermography in EEG-based classification of ASD. International Journal of Automation and Computing, vol. 17, no. 6, pp. 837–854, 2020. DOI: https://doi.org/10.1007/s11633-020-1231-6.

    Article  Google Scholar 

  35. I. D. Rubasinghe, D. A. Meedeniya. Automated neuroscience decision support framework. Deep Learning Techniques for Biomedical and Health Informatics, B. Agarwal, V. E. Balas, L. C. Jain, R. C. Poonia, Manisha, Eds., Amsterdam, Netherlands: Elsevier Academic Press, pp. 305–326, 2020. DOI: https://doi.org/10.1016/B978-0-12-819061-6.00013-6.

    Google Scholar 

  36. A. Diaz-Pinto, S. Morales, V. Naranjo, T. Köhler, J. M. Mossi, A. Navea. CNNs for automatic glaucoma assessment using fundus images: An extensive validation. Bio-Medical Engineering OnLine, vol. 18, no. 1, Article number 29, 2019. DOI: https://doi.org/10.1186/s12938-019-0649-y.

  37. K. A. Thakoor, X. H. Li, E. Tsamis, P. Sajda, D. C. Hood. Enhancing the accuracy of glaucoma detection from OCT probability maps using convolutional neural networks. In Proceedings of the 41st Annual International Conference of IEEE Engineering in Medicine and Biology Society, Berlin, Germany, pp. 2036–2040, 2019. DOI: https://doi.org/10.1109/embc.2019.8856899.

  38. S. Sreng, N. Maneerat, K. Hamamoto, K. Y. Win. Deep learning for optic disc segmentation and glaucoma diagnosis on retinal images. Applied Sciences, vol. 10, no. 14, Article number 4916, 2020. DOI: https://doi.org/10.3390/app10144916.

  39. M. N. Bajwa, M. I. Malik, S. A. Siddiqui, A. Dengel, F. Shafait, W. Neumeier, S. Ahmed. Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning. BMC Medical Informatics and Decision Making, vol. 19, no. 1, Article number 136, 2019. DOI: https://doi.org/10.1186/s12911-019-0842-8.

  40. H. N. Veena, A. Muruganandham, T. Senthil Kumaran. A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images. Journal of King Saud University — Computer and Information Sciences, to be published. DOI: https://doi.org/10.1016/j.jksuci.2021.02.003.

  41. S. De Silva, S. Dayarathna, G. Ariyarathne, D. Meedeniya, S. Jayarathna, A. M. P. Michalek. Computational decision support system for ADHD identification. International Journal of Automation and Computing, vol. 18, no. 2, pp. 233–255, 2021. DOI: https://doi.org/10.1007/s11633-020-1252-1.

    Article  Google Scholar 

  42. Z. A. Zhang, C. D. Wu, S. Coleman, D. Kerr. DENSE- INception U-net for medical image segmentation. Computer Methods and Programs in Biomedicine, vol. 192, Article number 105395, 2020. DOI: https://doi.org/10.1016/j.cmpb.2020.105395.

  43. Y. F. **ng, L. Zhong, X. Zhong. An encoder-decoder network based FCN architecture for semantic segmentation. Wireless Communications and Mobile Computing, vol. 2020, Article number 8861886, 2020. DOI: https://doi.org/10.1155/2020/8861886.

  44. M. J. Ankenbrand, L. Shainberg, M. Hock, D. Lohr, L. M. Schreiber. Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI. BMC Medical Imaging, vol. 21, no. 1, Article number 27, 2021. DOI: https://doi.org/10.1186/s12880-021-00551-1.

  45. A. Singh, S. Sengupta, V. Lakshminarayanan. Glaucoma diagnosis using transfer learning methods. In Proceedings of SPIE 11139, Applications of Machine Learning, San Diego, USA, pp. 1–11, 2019. DOI: https://doi.org/10.1117/12.2529429.

  46. Aziz-ur-Rehman, I. A. Taj, M. Sajid, K. S. Karimov. An ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography. Mathematical Biosciences and Engineering, vol. 18, no. 5, pp. 5321–5346, 2021. DOI: https://doi.org/10.3934/mbe.2021270.

    Article  MathSciNet  MATH  Google Scholar 

  47. S. Y. Li, Z. Li, L. M. Guo, G. B. Bian. Glaucoma Detection: Joint segmentation and classification framework via deep ensemble network. In Proceedings of the 5th International Conference on Advanced Robotics and Mechatronics, IEEE, Shenzhen, China, pp. 678–685, 2020. DOI: https://doi.org/10.1109/icarm49381.2020.9195312.

    Google Scholar 

  48. H. Z. Fu, J. Cheng, Y. W. Xu, C. Q. Zhang, D. W. K. Wong, J. Liu, X. C. Cao. Disc-aware ensemble network for glaucoma screening from fundus image. IEEE Transactions on Medical Imaging, vol. 37, no. 11, pp. 2493–2501, 2018. DOI: https://doi.org/10.1109/tmi.2018.2837012.

    Article  Google Scholar 

  49. J. I. Orlando, H. Z. Fu, B. Breda, K. van Keer, D. R. Bathula, A. Diaz-Pinto, R. G. Fang, P. A. Heng, J. Kim, J. H. Lee, J. Lee, X. X. Li, P. Liu, S. Lu, B. Murugesan, V. Naranjo, S. S. R. Phaye, S. M. Shankaranarayana, A. Sikka, J. Son, A. van den Hengel, S. J. Wang, J. Y. Wu, Z. F. Wu, G. H. Xu, Y. L. Xu, P. S. Yin, F. Li, X. L. Zhang, Y. W. Xu, H. Bogunovic. REFUGE challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Medical Image Analysis, vol. 59, Article number 101570, 2018. DOI: https://doi.org/10.1016/j.media.2019.101570.

  50. G. Lim, W. Hsu, M. L. Lee, D. S. W. Ting, T. Y. Wong. Technical and clinical challenges of AI in retinal image analysis. Computational Retinal Image Analysis: Tools, Applications and Perspectives, E. Trucco, T. MacGillivray, Y. W. Xu, Eds., Amsterdam, Netherlands: Elsevier Academic Press, pp. 445–466, 2019. DOI: https://doi.org/10.1016/b978-0-08-102816-2.00022-8.

    Chapter  Google Scholar 

  51. S. Dasanayaka, S. Silva, V. Shantha, D. Meedeniya, T. Ambegoda. Interpretable machine learning for brain tumor analysis using MRI. In Proceedings of the 2nd International Conference on Advanced Research in Computing, IEEE, Belihuloya, Sri Lanka, pp. 212–217, 2022. DOI: https://doi.org/10.1109/ICARC54489.2022.9754131.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Moratuwa, Katubedda, 10400, Sri Lanka

    Thisara Shyamalee & Dulani Meedeniya

Authors
  1. Thisara Shyamalee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dulani Meedeniya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dulani Meedeniya.

Additional information

Thisara Shyamalee received the B. Sc. degree in information technology from Sri Lanka Institute of Information Technology (SLIIT), Sri Lanka in 2019. She is currently a master student in computer science (with a major component of research) at Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka.

Her research interests include machine learning, deep learning, and computer interaction.

Dulani Meedeniya received the Ph. D. degree in computer science from University of St Andrews, UK in 2013. She is a professor in computer science and engineering at the University of Moratuwa, Sri Lanka. She is the director of the Bio-Health Informatics Group in her department and engages in many collaborative projects. She is a co-author of 100+ publications in indexed journals, peer-reviewed conferences and international book chapters. She has received several awards and grants for her contribution in research. She serves as a reviewer, program committee and editorial team member in many international conferences and journals. She is a fellow of HEA (UK), MIET, senior member IEEE, member ACM and a chartered engineer registered at EC (UK).

Her research interests include software modelling and design, bio-health informatics, deep learning and technology-enhanced learning.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shyamalee, T., Meedeniya, D. Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification. Mach. Intell. Res. 19, 563–580 (2022). https://doi.org/10.1007/s11633-022-1354-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-022-1354-z

Keywords

Search

Navigation