One of the main causes of irreversible blindness is glaucoma, which is characterized by certain alterations in the optic nerve that over time can impair vision. Manual evaluations, while invaluable, are challenged by inconsistencies, subjectivity, and the extensive time required. The study employed a dataset comprising retinal images annotated by experts. By using Convolutional Neural Networks (CNNs) to study and recognize important patterns in eye images, it was possible to accurately distinguish between optic nerves that were healthy and those that were damaged by glaucoma. To locate and classify glaucoma optic disks, this paper explores the emerging field of machine learning and deep learning models. We explore the methods, discoveries, and advancements that have influenced the field of automated glaucoma detection as we progress through the current research environment. The findings suggest that deep learning models can play a crucial role in the automated detection of glaucoma, supporting ophthalmologists in clinical decision-making and reducing the burden of manual diagnosis. Further research and clinical validation are recommended to refine these models and integrate them into routine clinical practice.

Keywords:Glaucoma, optic disk, machine learning, convolutional neural networks, diagnosis,
ResNet 101, and ResNet 152, RIM-ONE v3

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1. Kitaguchi Y, Hayakawa R, Kawashima R, Matsushita K, Tanaka H, Kawasaki R, et al. Deep-learning approach to detect childhood glaucoma based on periocular photograph. Scientific Reports. 2023 Jun 22;13(1):10141.
2. Selvathi D. Classification of ocular diseases using transfer learning approaches and glaucoma severity grading. In Computational Methods and Deep Learning for Ophthalmology 2023 Jan 1 (pp. 1–15). Academic Press.
3. Liu Y, Yip LW, Zheng Y, Wang L. Glaucoma screening using an attention-guided stereo ensemble network. Methods. 2022 Jun 1;202:14–21.
4. Guergueb T, Akhloufi MA. A review of deep learning techniques for glaucoma detection. SN Computer Science. 2023 Mar 23;4(3):274.
5. CH M. Glaucoma detection using convolution neural networks. In 2023 7th International Conference on Computing Methodologies and Communication (ICCMC) 2023 Feb 23 (pp. 368–372). IEEE.
6. Hemelings R, Elen B, Schuster AK, Blaschko MB, Barbosa-Breda J, Hujanen P, et al. A generalizable deep learning regression model for automated glaucoma screening from fundus images. NPJ digital medicine. 2023 Jun 13;6(1):112.
7. Varma N, Yadav S, Yadav JK. A short review on automatic detection of glaucoma using fundus image. Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2022, Volume 2. 2022 Sep 16:493–504.
8. Hussain R, Basak H. UT-Net: Combining U-Net and transformer for joint optic disc and cup segmentation and glaucoma detection. arXiv preprint arXiv:2303.04939. 2023 Mar 8.
9. Lenka S, Lazarus MZ, Behera SR. Glaucoma detection based on specularity removal low rank model from retinal fundus images. ECTI Transactions on Computer and Information Technology (ECTI-CIT). 2023 Jul 22;17(3):330–42.
10. Krishna N, Nagamani K. Multi-modal imaging-based feature fusion for accurate glaucoma diagnosis with deep learning.
11. Naidana KS, Barpanda SS. Glaucoma classification using a polynomial-driven deep learning approach. Bull Electr Eng Inform. 2023 Aug 1;12(4):2245–61.
12. Muramatsu C. Diagnosis of glaucoma on retinal fundus images using deep learning: Detection of nerve fiber layer defect and optic disc analysis. Deep learning in medical image analysis: challenges and applications. 2020:121–32.
13. Hasan MM, Phu J, Sowmya A, Meijering E, Kalloniatis M. Artificial intelligence in the diagnosis of glaucoma and neurodegenerative diseases. Clin Exp Optom. 2024 Feb 17;107(2):130–46.
14. Latif J, Tu S, Xiao C, Ur Rehman S, Imran A, Latif Y. ODGNet: a deep learning model for automated optic disc localization and glaucoma classification using fundus images. SN Applied Sciences. 2022 Apr;4(4):98.
15. Archana E, Geetha S, George GV. Short Analysis of Machine Learning and Deep Learning Techniques used for Glaucoma Detection. In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT) 2023 Jan 23 (pp. 968–975). IEEE.
16. Almansour A, Alawad M, Aljouie A, Almatar H, Qureshi W, Alabdulkader B, et al. Peripapillary atrophy classification using CNN deep learning for glaucoma screening. Plos one. 2022 Oct 6;17(10):e0275446.
17. Rasheed HA, Davis T, Morales E, Fei Z, Grassi L, De Gainza A, Nouri-Mahdavi K, Caprioli J. DDLSNet: A novel deep learning-based system for grading funduscopic images for glaucomatous damage. Ophthalmology Science. 2023 Jun 1;3(2):100255.
18. Zedan MJ, Zulkifley MA, Ibrahim AA, Moubark AM, Kamari NA, Abdani SR. Automated glaucoma screening and diagnosis based on retinal fundus images using deep learning approaches: A comprehensive review. Diagnostics. 2023 Jun 26;13(13):2180.
19. Charng J, Alam K, Swartz G, Kugelman J, Alonso-Caneiro D, Mackey DA, et al. Deep learning: Applications in retinal and optic nerve diseases. Clinical and Experimental Optometry. 2023 Jul 4;106(5):466–75.
20. Fan R, Bowd C, Christopher M, Brye N, Proudfoot JA, Rezapour J, et al. Detecting glaucoma in the ocular hypertension study using deep learning. JAMA Ophthalmology. 2022 Apr 1;140(4):383–91.
21. Nawaz M, Nazir T, Javed A, Tariq U, Yong HS, Khan MA, et al. An efficient deep learning approach to automatic glaucoma detection using optic disc and optic cup localization. Sensors. 2022 Jan 7;22(2):434.
22. Ahmed A, Jammal AA, Estrela T, Berchuck SI, Medeiros FA. Intraocular pressure and rates of macular thinning in glaucoma. Ophthalmology Glaucoma. 2023 Sep 1;6(5):457–65.

One of the main causes of irreversible blindness is glaucoma, which is characterized by certain alterations in the optic nerve that over time can impair vision. Manual evaluations, while invaluable, are challenged by inconsistencies, subjectivity, and the extensive time required. The study employed a dataset comprising retinal images annotated by experts. By using Convolutional Neural Networks (CNNs) to study and recognize important patterns in eye images, it was possible to accurately distinguish between optic nerves that were healthy and those that were damaged by glaucoma. To locate and classify glaucoma optic disks, this paper explores the emerging field of machine learning and deep learning models. We explore the methods, discoveries, and advancements that have influenced the field of automated glaucoma detection as we progress through the current research environment. The findings suggest that deep learning models can play a crucial role in the automated detection of glaucoma, supporting ophthalmologists in clinical decision-making and reducing the burden of manual diagnosis. Further research and clinical validation are recommended to refine these models and integrate them into routine clinical practice.

Keywords:Glaucoma, optic disk, machine learning, convolutional neural networks, diagnosis,
ResNet 101, and ResNet 152, RIM-ONE v3

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Kitaguchi Y, Hayakawa R, Kawashima R, Matsushita K, Tanaka H, Kawasaki R, et al. Deep-learning approach to detect childhood glaucoma based on periocular photograph. Scientific Reports. 2023 Jun 22;13(1):10141.
2. Selvathi D. Classification of ocular diseases using transfer learning approaches and glaucoma severity grading. In Computational Methods and Deep Learning for Ophthalmology 2023 Jan 1 (pp. 1–15). Academic Press.
3. Liu Y, Yip LW, Zheng Y, Wang L. Glaucoma screening using an attention-guided stereo ensemble network. Methods. 2022 Jun 1;202:14–21.
4. Guergueb T, Akhloufi MA. A review of deep learning techniques for glaucoma detection. SN Computer Science. 2023 Mar 23;4(3):274.
5. CH M. Glaucoma detection using convolution neural networks. In 2023 7th International Conference on Computing Methodologies and Communication (ICCMC) 2023 Feb 23 (pp. 368–372). IEEE.
6. Hemelings R, Elen B, Schuster AK, Blaschko MB, Barbosa-Breda J, Hujanen P, et al. A generalizable deep learning regression model for automated glaucoma screening from fundus images. NPJ digital medicine. 2023 Jun 13;6(1):112.
7. Varma N, Yadav S, Yadav JK. A short review on automatic detection of glaucoma using fundus image. Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2022, Volume 2. 2022 Sep 16:493–504.
8. Hussain R, Basak H. UT-Net: Combining U-Net and transformer for joint optic disc and cup segmentation and glaucoma detection. arXiv preprint arXiv:2303.04939. 2023 Mar 8.
9. Lenka S, Lazarus MZ, Behera SR. Glaucoma detection based on specularity removal low rank model from retinal fundus images. ECTI Transactions on Computer and Information Technology (ECTI-CIT). 2023 Jul 22;17(3):330–42.
10. Krishna N, Nagamani K. Multi-modal imaging-based feature fusion for accurate glaucoma diagnosis with deep learning.
11. Naidana KS, Barpanda SS. Glaucoma classification using a polynomial-driven deep learning approach. Bull Electr Eng Inform. 2023 Aug 1;12(4):2245–61.
12. Muramatsu C. Diagnosis of glaucoma on retinal fundus images using deep learning: Detection of nerve fiber layer defect and optic disc analysis. Deep learning in medical image analysis: challenges and applications. 2020:121–32.
13. Hasan MM, Phu J, Sowmya A, Meijering E, Kalloniatis M. Artificial intelligence in the diagnosis of glaucoma and neurodegenerative diseases. Clin Exp Optom. 2024 Feb 17;107(2):130–46.
14. Latif J, Tu S, Xiao C, Ur Rehman S, Imran A, Latif Y. ODGNet: a deep learning model for automated optic disc localization and glaucoma classification using fundus images. SN Applied Sciences. 2022 Apr;4(4):98.
15. Archana E, Geetha S, George GV. Short Analysis of Machine Learning and Deep Learning Techniques used for Glaucoma Detection. In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT) 2023 Jan 23 (pp. 968–975). IEEE.
16. Almansour A, Alawad M, Aljouie A, Almatar H, Qureshi W, Alabdulkader B, et al. Peripapillary atrophy classification using CNN deep learning for glaucoma screening. Plos one. 2022 Oct 6;17(10):e0275446.
17. Rasheed HA, Davis T, Morales E, Fei Z, Grassi L, De Gainza A, Nouri-Mahdavi K, Caprioli J. DDLSNet: A novel deep learning-based system for grading funduscopic images for glaucomatous damage. Ophthalmology Science. 2023 Jun 1;3(2):100255.
18. Zedan MJ, Zulkifley MA, Ibrahim AA, Moubark AM, Kamari NA, Abdani SR. Automated glaucoma screening and diagnosis based on retinal fundus images using deep learning approaches: A comprehensive review. Diagnostics. 2023 Jun 26;13(13):2180.
19. Charng J, Alam K, Swartz G, Kugelman J, Alonso-Caneiro D, Mackey DA, et al. Deep learning: Applications in retinal and optic nerve diseases. Clinical and Experimental Optometry. 2023 Jul 4;106(5):466–75.
20. Fan R, Bowd C, Christopher M, Brye N, Proudfoot JA, Rezapour J, et al. Detecting glaucoma in the ocular hypertension study using deep learning. JAMA Ophthalmology. 2022 Apr 1;140(4):383–91.
21. Nawaz M, Nazir T, Javed A, Tariq U, Yong HS, Khan MA, et al. An efficient deep learning approach to automatic glaucoma detection using optic disc and optic cup localization. Sensors. 2022 Jan 7;22(2):434.
22. Ahmed A, Jammal AA, Estrela T, Berchuck SI, Medeiros FA. Intraocular pressure and rates of macular thinning in glaucoma. Ophthalmology Glaucoma. 2023 Sep 1;6(5):457–65.