Skip to main content

Deep Learning Glaucoma Detection Models in Retinal Images Capture by Mobile Devices

  • Conference paper
  • First Online:
Wireless Mobile Communication and Healthcare (MobiHealth 2022)

Abstract

Glaucoma is a disease that arises from increased intraocular pressure and leads to irreversible partial or total loss of vision. Due to the lack of symptoms, this disease often progresses to more advanced stages, not being detected in the early phase. The screening of glaucoma can be made through visualization of the retina, through retinal images captured by medical equipment or mobile devices with an attached lens to the camera. Deep learning can enhance and increase mass glaucoma screening. In this study, domain transfer learning technique is important to better weight initialization and for understanding features more related to the problem. For this, classic convolutional neural networks, such as ResNet50 will be compared with Vision Transformers, in high and low-resolution images. The high-resolution retinal image will be used to pre-trained the network and use that knowledge for detecting glaucoma in retinal images captured by mobile devices. The ResNet50 model reached the highest values of AUC in the high-resolution dataset, being the more consistent model in all the experiments. However, the Vision Transformer proved to be a promising technique, especially in low-resolution retinal images.

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
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

Similar content being viewed by others

References

  1. Claro, M.L., Veras, R., Santos, L., Frazão, M., Carvalho Filho, A., Leite, D.: Métodos computacionais para segmentação do disco óptico em imagens de retina: uma revisão. Rev. Bras. Comput. Apl. 10(2), 29–43 (2018). https://doi.org/10.5335/rbca.v10i2.7661

  2. Bajwa, M.N., et al.: Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning. BMC Med. Inform. Decis. Mak. 19(1), 136 (2019). https://doi.org/10.1186/s12911-019-0842-8

    Article  MathSciNet  Google Scholar 

  3. Stella Mary, M.C.V., Rajsingh, E.B., Naik, G.R.: Retinal fundus image analysis for diagnosis of glaucoma: a comprehensive survey. IEEE Access 4, 4327–4354 (2016). https://doi.org/10.1109/ACCESS.2016.2596761

  4. Hagiwara, Y., et al.: Computer-aided diagnosis of glaucoma using fundus images: a review. Comput. Methods Programs Biomed. 165, 1–12 (2018). https://doi.org/10.1016/j.cmpb.2018.07.012

    Article  Google Scholar 

  5. Camara, J., Neto, A., Pires, I.M., Villasana, M.V., Zdravevski, E., Cunha, A.: A comprehensive review of methods and equipment for aiding automatic glaucoma tracking. Diagnostics 12(4), 935 (2022). https://doi.org/10.3390/diagnostics12040935

    Article  Google Scholar 

  6. Mayro, E.L., Wang, M., Elze, T., Pasquale, L.R.: The impact of artificial intelligence in the diagnosis and management of glaucoma. Eye 34(1), 1–11 (2019). https://doi.org/10.1038/s41433-019-0577-x

    Article  Google Scholar 

  7. Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017). https://doi.org/10.1016/j.media.2017.07.005

    Article  Google Scholar 

  8. Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights Imaging 9(4), 611–629 (2018). https://doi.org/10.1007/s13244-018-0639-9

    Article  Google Scholar 

  9. Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., Gao, R.X.: Deep learning and its applications to machine health monitoring. Mech. Syst. Signal Process. 115, 213–237 (2019). https://doi.org/10.1016/j.ymssp.2018.05.050

    Article  Google Scholar 

  10. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition arXiv, arXiv:1512.03385 (2015). Accessed 05 June 2022

  11. Dosovitskiy, A., et al.: An image is worth 16 × 16 words: transformers for image recognition at scale. arXiv, arXiv:2010.11929 (2021). Accessed 02 June 2022

  12. Neto, A., Camara, J., Cunha, A.: Evaluations of deep learning approaches for glaucoma screening using retinal images from mobile device. Sensors 22(4), 1449 (2022). https://doi.org/10.3390/s22041449

    Article  Google Scholar 

  13. Linardatos, P., Papastefanopoulos, V., Kotsiantis, S.: Explainable AI: a review of machine learning interpretability methods. Entropy 23(1), 1–45 (2021). https://doi.org/10.3390/e23010018

    Article  Google Scholar 

  14. Abnar, S., Zuidema, W.: Quantifying attention flow in transformers (2020). http://arxiv.org/abs/2005.00928. Accessed 18 July 2022

  15. Chen, X., Xu, Y., Kee Wong, D.W., Wong, T.Y., Liu, J.: Glaucoma detection based on deep convolutional neural network. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, pp. 715–718 (2015). https://doi.org/10.1109/EMBC.2015.7318462

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

    Article  MathSciNet  Google Scholar 

  17. Chai, Y., Liu, H., Xu, J.: Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models. Knowl.-Based Syst. 161, 147–156 (2018). https://doi.org/10.1016/j.knosys.2018.07.043

    Article  Google Scholar 

  18. Benzebouchi, N.E., Azizi, N., Bouziane, S.E.: Glaucoma diagnosis using cooperative convolutional neural networks. Int. J. Adv. Electron. Comput. Sci. 5(1), 31–36 (2018)

    Google Scholar 

  19. Suguna, G., Lavanya, R.: Performance assessment of EyeNet model in glaucoma diagnosis. Pattern Recogn. Image Anal. 31(2), 334–344 (2021). https://doi.org/10.1134/S1054661821020164

    Article  Google Scholar 

  20. Alghamdi, M., Abdel-Mottaleb, M.: A Comparative study of deep learning models for diagnosing glaucoma from fundus images. IEEE Access 9, 23894–23906 (2021). https://doi.org/10.1109/ACCESS.2021.3056641

    Article  Google Scholar 

  21. Fumero Batista, F.J., Diaz-Aleman, T., Sigut, J., Alayon, S., Arnay, R., Angel-Pereira, D.: RIM-ONE DL: a unified retinal image database for assessing glaucoma using deep learning. Image Anal. Stereol. 39(3), Article no. 3 (2020). https://doi.org/10.5566/ias.2346

  22. Sivaswamy, J., Krishnadas, S.R., Datt Joshi, G., Jain, M., Syed Tabish, A.U.: Drishti-GS: retinal image dataset for optic nerve head (ONH) segmentation. Presented at the 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 53–56 (2014). https://doi.org/10.1109/ISBI.2014.6867807

  23. Fu, H.: REFUGE: Retinal Fundus Glaucoma Challenge. IEEE (2019). https://ieee-dataport.org/documents/refuge-retinal-fundus-glaucoma-challenge. Accessed 16 Nov 2022

  24. Bargoti, S., Underwood, J.: Deep fruit detection in orchards (2017). http://arxiv.org/abs/1610.03677. Accessed 28 July 2022

Download references

Acknowledgements

This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to António Cunha .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rezende, R.F., Coelho, A., Fernandes, R., Camara, J., Neto, A., Cunha, A. (2023). Deep Learning Glaucoma Detection Models in Retinal Images Capture by Mobile Devices. In: Cunha, A., M. Garcia, N., Marx Gómez, J., Pereira, S. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 484. Springer, Cham. https://doi.org/10.1007/978-3-031-32029-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-32029-3_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-32028-6

  • Online ISBN: 978-3-031-32029-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics