Deep Learning in Glaucoma with Fundus Photography
Research Article
Open Access
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Deep Learning in Glaucoma with Fundus Photography

Yidan Zhang 1*
1 Glasgow College, University of Electronic Science and Technology of China, Chengdu, China
*Corresponding author: 2024190905011@std.uestc.edu.cn
Published on 26 November 2025
Volume Cover
ACE Vol.210
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-567-7
ISBN (Online): 978-1-80590-568-4
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Abstract

Deep learning (DL) is a key branch of artificial intelligence (AI). It has made remarkable progress in medical imaging, particularly in image classification and pattern recognition. In ophthalmology, the application of DLto fundus images for glaucoma assessment has become a rapidly developing research area. In recent years, this approach has represented promising results in terms of both analytical efficiency and accuracy, assisting in the differentiation of glaucoma patients from healthy eyes. This trend suggests that DL technology has the potential to improve existing diagnostic and treatment practices and optimize the clinical workflow for glaucoma diagnosis. However, challenges remain, such as a shortage of high-quality annotated data and limited model interpretability. This article reviews recent research progress on DL-based glaucoma assessment using fundus images, explores its potential clinical significance, and proposes future research directions. Through a systematic review of relevant literature, this study provides a comprehensive understanding of the application of DL in glaucoma diagnosis for both academia and clinical practice, and offers reference and guidance for future research.

Keywords:

Artificial intelligence, Deep learning, Gaucoma

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Zhang,Y. (2025). Deep Learning in Glaucoma with Fundus Photography. Applied and Computational Engineering,210,51-56.

References

[1]. Jayaram, H., Kolko, M., Friedman, D. S., & Gazzard, G. (2023). Glaucoma: Now and beyond. The Lancet, 402(10414), 1788–1801. https: //doi.org/10.1016/S0140-6736(23)01523-7

[2]. Schuster, A. K., Erb, C., Hoffmann, E. M., Dietlein, T., & Pfeiffer, N. (2020). The diagnosis and treatment of glaucoma. Deutsches Ärzteblatt International, 117(13), 225–234. https: //doi.org/10.3238/arztebl.2020.0225

[3]. Mursch-Edlmayr, A. S., Ng, W. S., Diniz-Filho, A., et al. (2020). Artificial intelligence algorithms to diagnose glaucoma and detect glaucoma progression: Translation to clinical practice. Translational Vision Science & Technology, 9(2), 55. https: //doi.org/10.1167/tvst.9.2.55

[4]. Ashtari-Majlan, M., Dehshibi, M. M., & Masip, D. (2023). Deep learning and computer vision for glaucoma detection: A review. arXiv preprint arXiv: 2307.16528. https: //doi.org/10.48550/arXiv.2307.16528

[5]. Panwar, N., Huang, P., Lee, J., Keane, P. A., Chuan, T. S., Richhariya, A., ... & Agrawal, R. (2016). Fundus photography in the 21st century—A review of recent technological advances and their implications for worldwide healthcare. Telemedicine and e-Health, 22(3), 198–208. https: //doi.org/10.1089/tmj.2015.0068

[6]. Raghavendra, U., Fujita, H., Bhandary, S. V., Gudigar, A., Tan, J. H., & Acharya, U. R. (2018). Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Information Sciences, 441, 41–49. https: //doi.org/10.1016/j.ins.2018.02.074

[7]. Deperlioglu, O., Kose, U., Gupta, D., Khanna, A., Giampaolo, F., & Fortino, G. (2022). Explainable framework for glaucoma diagnosis by image processing and convolutional neural network synergy: Analysis with doctor evaluation. Future Generation Computer Systems, 129, 152–169. https: //doi.org/10.1016/j.future.2021.11.011

[8]. Chen, X., Xu, Y., Wong, D. W. K., Wong, T. Y., & Liu, J. (2015, August). Glaucoma detection based on deep convolutional neural network. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 715–718). IEEE. https: //doi.org/10.1109/EMBC.2015.7318392

[9]. Christopher, M., Belghith, A., Bowd, C., Proudfoot, J. A., Goldbaum, M. H., Weinreb, R. N., ... & Zangwill, L. M. (2018). Performance of deep learning architectures and transfer learning for detecting glaucomatous optic neuropathy in fundus photographs. Scientific Reports, 8(1), 16685. https: //doi.org/10.1038/s41598-018-35044-9

[10]. Shibata, N., Tanito, M., Mitsuhashi, K., Fujino, Y., Matsuura, M., Murata, H., & Asaoka, R. (2018). Development of a deep residual learning algorithm to screen for glaucoma from fundus photography. Scientific Reports, 8(1), 14665. https: //doi.org/10.1038/s41598-018-32861-5

[11]. Zhao, R., Liao, W., Zou, B., Chen, Z., & Li, S. (2019, July). Weakly-supervised simultaneous evidence identification and segmentation for automated glaucoma diagnosis. In Proceedings of the AAAI Conference on Artificial Intelligence, 33(1), 809–816. https: //doi.org/10.1609/aaai.v33i01.3301809

[12]. Diaz-Pinto, A., Colomer, A., Naranjo, V., Morales, S., Xu, Y., & Frangi, A. F. (2019). Retinal image synthesis and semi-supervised learning for glaucoma assessment. IEEE Transactions on Medical Imaging, 38(9), 2211–2218. https: //doi.org/10.1109/TMI.2019.2902391

[13]. Navab, N., Hornegger, J., Wells, W. M., & Frangi, A. (Eds.). (2015). Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5–9, 2015, proceedings, part III (Vol. 9351). Springer. https: //doi.org/10.1007/978-3-319-24574-4

[14]. Chai, Y., Liu, H., & Xu, J. (2018). Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models. Knowledge-Based Systems, 161, 147–156.

[15]. Akter, N., Fletcher, J., Perry, S., Simunovic, M. P., Briggs, N., & Roy, M. (2022). Glaucoma diagnosis using multi-feature analysis and a deep learning technique. Scientific Reports, 12(1), 8064. https: //doi.org/10.1038/s41598-022-12122-9

[16]. Medeiros, F. A., Jammal, A. A., & Thompson, A. C. (2019). From machine to machine: an OCT-trained deep learning algorithm for objective quantification of glaucomatous damage in fundus photographs. Ophthalmology, 126(4), 513-521.

References

[1]. Jayaram, H., Kolko, M., Friedman, D. S., & Gazzard, G. (2023). Glaucoma: Now and beyond. The Lancet, 402(10414), 1788–1801. https: //doi.org/10.1016/S0140-6736(23)01523-7

[2]. Schuster, A. K., Erb, C., Hoffmann, E. M., Dietlein, T., & Pfeiffer, N. (2020). The diagnosis and treatment of glaucoma. Deutsches Ärzteblatt International, 117(13), 225–234. https: //doi.org/10.3238/arztebl.2020.0225

[3]. Mursch-Edlmayr, A. S., Ng, W. S., Diniz-Filho, A., et al. (2020). Artificial intelligence algorithms to diagnose glaucoma and detect glaucoma progression: Translation to clinical practice. Translational Vision Science & Technology, 9(2), 55. https: //doi.org/10.1167/tvst.9.2.55

[4]. Ashtari-Majlan, M., Dehshibi, M. M., & Masip, D. (2023). Deep learning and computer vision for glaucoma detection: A review. arXiv preprint arXiv: 2307.16528. https: //doi.org/10.48550/arXiv.2307.16528

[5]. Panwar, N., Huang, P., Lee, J., Keane, P. A., Chuan, T. S., Richhariya, A., ... & Agrawal, R. (2016). Fundus photography in the 21st century—A review of recent technological advances and their implications for worldwide healthcare. Telemedicine and e-Health, 22(3), 198–208. https: //doi.org/10.1089/tmj.2015.0068

[6]. Raghavendra, U., Fujita, H., Bhandary, S. V., Gudigar, A., Tan, J. H., & Acharya, U. R. (2018). Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Information Sciences, 441, 41–49. https: //doi.org/10.1016/j.ins.2018.02.074

[7]. Deperlioglu, O., Kose, U., Gupta, D., Khanna, A., Giampaolo, F., & Fortino, G. (2022). Explainable framework for glaucoma diagnosis by image processing and convolutional neural network synergy: Analysis with doctor evaluation. Future Generation Computer Systems, 129, 152–169. https: //doi.org/10.1016/j.future.2021.11.011

[8]. Chen, X., Xu, Y., Wong, D. W. K., Wong, T. Y., & Liu, J. (2015, August). Glaucoma detection based on deep convolutional neural network. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 715–718). IEEE. https: //doi.org/10.1109/EMBC.2015.7318392

[9]. Christopher, M., Belghith, A., Bowd, C., Proudfoot, J. A., Goldbaum, M. H., Weinreb, R. N., ... & Zangwill, L. M. (2018). Performance of deep learning architectures and transfer learning for detecting glaucomatous optic neuropathy in fundus photographs. Scientific Reports, 8(1), 16685. https: //doi.org/10.1038/s41598-018-35044-9

[10]. Shibata, N., Tanito, M., Mitsuhashi, K., Fujino, Y., Matsuura, M., Murata, H., & Asaoka, R. (2018). Development of a deep residual learning algorithm to screen for glaucoma from fundus photography. Scientific Reports, 8(1), 14665. https: //doi.org/10.1038/s41598-018-32861-5

[11]. Zhao, R., Liao, W., Zou, B., Chen, Z., & Li, S. (2019, July). Weakly-supervised simultaneous evidence identification and segmentation for automated glaucoma diagnosis. In Proceedings of the AAAI Conference on Artificial Intelligence, 33(1), 809–816. https: //doi.org/10.1609/aaai.v33i01.3301809

[12]. Diaz-Pinto, A., Colomer, A., Naranjo, V., Morales, S., Xu, Y., & Frangi, A. F. (2019). Retinal image synthesis and semi-supervised learning for glaucoma assessment. IEEE Transactions on Medical Imaging, 38(9), 2211–2218. https: //doi.org/10.1109/TMI.2019.2902391

[13]. Navab, N., Hornegger, J., Wells, W. M., & Frangi, A. (Eds.). (2015). Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5–9, 2015, proceedings, part III (Vol. 9351). Springer. https: //doi.org/10.1007/978-3-319-24574-4

[14]. Chai, Y., Liu, H., & Xu, J. (2018). Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models. Knowledge-Based Systems, 161, 147–156.

[15]. Akter, N., Fletcher, J., Perry, S., Simunovic, M. P., Briggs, N., & Roy, M. (2022). Glaucoma diagnosis using multi-feature analysis and a deep learning technique. Scientific Reports, 12(1), 8064. https: //doi.org/10.1038/s41598-022-12122-9

[16]. Medeiros, F. A., Jammal, A. A., & Thompson, A. C. (2019). From machine to machine: an OCT-trained deep learning algorithm for objective quantification of glaucomatous damage in fundus photographs. Ophthalmology, 126(4), 513-521.

Cite this article

Zhang,Y. (2025). Deep Learning in Glaucoma with Fundus Photography. Applied and Computational Engineering,210,51-56.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-567-7(Print) / 978-1-80590-568-4(Online)
Editor: Hisham AbouGrad
Conference website: https://www.confmla.org/london.html
Conference date: 12 November 2025
Series: Applied and Computational Engineering
Volume number: Vol.210
ISSN: 2755-2721(Print) / 2755-273X(Online)

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