Skip to main content

Automated Diagnosis of Retinal Neovascularization Pathologies from Color Retinal Fundus Images

  • Conference paper
  • First Online:
Advances in Computer Graphics (CGI 2022)

Abstract

The retinal Neo-Vascularization (NV) is the abnormal growth of new blood vessels in the retina, which leads to a severe reduction on visual acuity and blindness. It is a main biomarker to screening several diseases, where the Proliferative Diabetic Retinopathy (PDR) and Wet Age-related Macular Degeneration (WAMD) are the most common ones. The NV severity requires a fast screening to avoid severe degradation. However, it is labor intensive and time-consuming for the ophthalmologists.

In this paper, we suggest an automated screening method that automatically detects NV from fundus photography and classify it as PDR, WAMD and Healthy. For this purpose, the image is preprocessed and then provided a transfer learned model of the VGG16 neural network. The method was evaluated using a dataset containing 395 fundus photographs of retinal images where an accuracy of 98.30%, a sensitivity of 98.66%, a specificity of 98.33% were achieved. In addition, the areas under curve in terms of classes were between 98% and 100%.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Yu, S., Xiao, D., Kanagasingam, Y.: Machine learning based automatic neovascularization detection on optic disc region. IEEE J. Biomed. Health Inform. 22(3), 886–894 (2018). https://doi.org/10.1109/JBHI.2017.2710201

  2. Wong, W.L., et al.: Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Global Health 2(2), e106–e116 (2014). https://doi.org/10.1016/S2214-109X(13)70145-1

  3. International Diabetes Federation. International diabetes federation diabetes atlas.. https://www.diabetesatlas.org/en/

  4. Elloumi, Y., Abroug, N., Bedoui, M.H.: End-to-end mobile system for diabetic retinopathy screening based on lightweight deep neural network. In: Bouadi, T., Fromont, E., Hüllermeier, E. (eds.) IDA 2022. LNCS, vol. 13205, pp. 66–77. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-01333-1_6

    Chapter  Google Scholar 

  5. Boukadida, R., Elloumi, Y., Akil, M., Bedoui, M.H.: Mobile‐aided screening system for proliferative diabetic retinopathy. Int. J. Imaging Syst. Technol. 31(3), 1638–1654 (2021). https://doi.org/10.1002/ima.22547

  6. Elloumi, Y., Ben Mbarek, M., Boukadida, R., Akil, M., Bedoui, M.H.: Fast and accurate mobile-aided screening system of moderate diabetic retinopathy. In: Thirteenth International Conference on Machine Vision, Rome, Italy, p. 93. January 2021. https://doi.org/10.1117/12.2588505

  7. Sayadia, S.B., Elloumi, Y., Kachouri, R., Akil, M., Abdallah, A.B., Bedoui, M.H.: Automated method for real-time AMD screening of fundus images dedicated for mobile devices. Med. Biol. Eng. Compu. 60(5), 1449–1479 (2022). https://doi.org/10.1007/s11517-022-02546-8

    Article  Google Scholar 

  8. Elloumi, Y., Akil, M., Boudegga, H.: Ocular diseases diagnosis in fundus images using a deep learning: approaches, tools and performance evaluation. In: Real-Time Image Processing and Deep Learning 2019, Baltimore, United States, p. 30, May 2019. https://doi.org/10.1117/12.2519098

  9. Peng, Y., et al.: DeepSeeNet: a deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs. Ophthalmology 126(4), 565–575 (2019). https://doi.org/10.1016/j.ophtha.2018.11.015

  10. Grassmann, F., et al.: A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology 125(9), 1410–1420 (2018). https://doi.org/10.1016/j.ophtha.2018.02.037

  11. Keel, S., et al.: Development and validation of a deep‐learning algorithm for the detection of neovascular age‐related macular degeneration from colour fundus photographs. Clin. Experiment. Ophthalmol. 47(8), 1009–1018 (2019). https://doi.org/10.1111/ceo.13575

  12. Heo, T.-Y., et al.: Development of a deep-learning-based artificial intelligence tool for differential diagnosis between dry and neovascular age-related macular degeneration. Diagnostics 10(5), 261 (2020). https://doi.org/10.3390/diagnostics10050261

  13. Burlina, P., Pacheco, K.D., Joshi, N., Freund, D.E., Bressler, N.M.: Comparing humans and deep learning performance for grading AMD: a study in using universal deep features and transfer learning for automated AMD analysis. Comput. Biol. Med. 82, 80–86 (2017). https://doi.org/10.1016/j.compbiomed.2017.01.018

  14. Pratt, H., Coenen, F., Broadbent, D.M., Harding, S.P., Zheng, Y.: Convolutional Neural networks for diabetic retinopathy. Procedia Comput. Sci. 90, 200–205 (2016). https://doi.org/10.1016/j.procs.2016.07.014

  15. Shanthi, T., Sabeenian, R.S.: Modified Alexnet architecture for classification of diabetic retinopathy images. Comput. Electr. Eng. 76, 56–64 (2019). https://doi.org/10.1016/j.compeleceng.2019.03.004

  16. Riaz, H., Park, J., Choi, H., Kim, H., Kim, J.: Deep and densely connected networks for classification of diabetic retinopathy. Diagnostics 10(1), 24 (2020). https://doi.org/10.3390/diagnostics10010024

  17. Wan, S., Liang, Y., Zhang, Y.: Deep convolutional neural networks for diabetic retinopathy detection by image classification. Comput. Electr. Eng. 72, 274–282 (2018). https://doi.org/10.1016/j.compeleceng.2018.07.042

  18. Liu, R., et al.: DeepDRiD: diabetic retinopathy—grading and image quality estimation challenge. Patterns 3(6), 100512 (2022). https://doi.org/10.1016/j.patter.2022.100512

  19. Dai, L., et al.: A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat. Commun. 12(1), 3242 (2021). https://doi.org/10.1038/s41467-021-23458-5

  20. Ghebrechristos, H., Alaghband, G., Hwang, R.Y.: RetiNet — feature extractor for learning patterns of diabetic retinopathy and age-related macular degeneration from publicly available datasets. In: 2017 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, pp. 1643–1648. December 2017. https://doi.org/10.1109/CSCI.2017.286

  21. González‐Gonzalo, C., et al.: Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age‐related macular degeneration », Acta Ophthalmology 98(4), pp. 368‑377 (2020. https://doi.org/10.1111/aos.14306

  22. Simonyan, K., Zisserman, A.: Very Deep convolutional networks for large-scale image Recognition (2014). https://doi.org/10.48550/ARXIV.1409.1556

  23. Boudegga, H., Elloumi, Y., Akil, M., Hedi Bedoui, M., Kachouri, R., Abdallah, A.B.: Fast and efficient retinal blood vessel segmentation method based on deep learning network. Comput. Med. Imag. Graph 90 101902 (2021). https://doi.org/10.1016/j.compmedimag.2021.101902

  24. OIA-ODIR: [En ligne]. Disponible sur: https://odir2019.grand-challenge.org

  25. RFMid: https://riadd.grand-challenge.org/download-all-classes/

  26. refuge-AMD. https://refuge.grand-challenge.org/iChallenge-AMD/

  27. Castillo Benítez, V.E., et al.: Dataset from fundus images for the study of diabetic retinopathy. Data in Brief 36, 107068 (2021). https://doi.org/10.1016/j.dib.2021.107068

  28. Elloumi, Y.: Cataract grading method based on deep convolutional neural networks and stacking ensemble learning. Int. J. Imaging Syst. Tech. 32(3), 798–814 (2022). https://doi.org/10.1002/ima.22722

  29. Mrad, Y., Elloumi, Y., Akil, Y., Bedoui, M.H.: Fast and accurate method for glaucoma screening from smartphone-captured fundus images. IRBM 43, 279–289 (2021). https://doi.org/10.1016/j.irbm.2021.06.004

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rahma Boukadida .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 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

Boukadida, R., Elloumi, Y., Kachouri, R., Abdallah, A.B., Bedoui, M.H. (2022). Automated Diagnosis of Retinal Neovascularization Pathologies from Color Retinal Fundus Images. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2022. Lecture Notes in Computer Science, vol 13443. Springer, Cham. https://doi.org/10.1007/978-3-031-23473-6_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-23473-6_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23472-9

  • Online ISBN: 978-3-031-23473-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics