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Research Article
Glaucoma Diagnosis Using Convolutional Neural Networks (CNNs)
A. Alagar1
A. Annalakshmi2
D. Beninal3
1Assistant Professor, Department of Computer Science and Engineering, K.L.N. College of Engineering and Technology, Sivagangai, Tamilnadu, India. 23Final Year Students, Department of Computer Science and Engineering, K.L.N. College of Engineering and Technology, Sivagangai, Tamilnadu, India.
Published Online: November-December 2024
Pages: 11-13
Cite this article
No DOIReferences
1. M. K. G. Shih, et al, "Deep Learning for Glaucoma Diagnosis: A Comprehensive Review." Ophthalmology, vol. 130, no. 5, pp. 1121–
1132, 2023.
2. C. Bowd, A. Belghith, L. M. Zangwill, M. Christopher, M. H. Goldbaum, 685 R. Fan, J. Rezapour, S. Moghimi, A. Kamalipour, H.
Hou, and 686 R. N. Weinreb, ‘‘Deep learning image analysis of optical coherence tomography angiography measured vessel density
improves classification of healthy and glaucoma eyes,’’ Amer. J. Ophthalmol., vol. 236, pp. 298–308, 689 Apr. 2022.
3. R. Ali, et al, "A Novel Convolutional Neural Network Approach for Early Detection of Glaucoma Using OCT Images." IEEE
Transactions on Biomedical Engineering, vol. 70, no. 4, pp. 987–994, 2023
4. T. Wang, et al, "Enhancing Glaucoma Detection Using Advanced CNN Techniques with Multi-modal Imaging." American Journal of
Ophthalmology, vol. 240, pp. 102–110, 2024.
5. A. Kamalipour, S. Moghimi, H. Hou, R. C. Penteado, W. H. Oh, 710 J. A. Proudfoot, N. El-Nimri, E. Ekici, J. Rezapour, L. M. Zangwill,
711 C. Bowd, and R. N. Weinreb, ‘‘OCT angiography artifacts in glaucoma,’’ 712 Ophthalmology, vol. 128, no. 10, pp. 1426–1437,
Oct. 2021.
6. Harris, G. Guidoboni, B. Siesky, S. Mathew, A. C. Verticchio Vercellin, 570 L. Rowe, and J. Arciero, ‘‘Ocular blood flow as a clinical
observation: 571 Value, limitations and data analysis,’’ Prog. Retinal Eye Res., vol. 78, 572 Sep. 2020, Art. no. 100841.
7. K. Liu, et al, "Multimodal Deep Learning for Enhanced Glaucoma Detection: Combining Fundus and OCT Images." IEEE
Transactions on Medical Imaging, vol. 43, no. 2, pp. 300–309, 2024.
8. J. Doe, et al, "Application of Convolutional Neural Networks in Detecting Glaucoma from OCT Images." Ophthalmic Surgery, Lasers
& Imaging Retina, vol. 54, no. 2, pp. 123–130, 2023.
9. A. C. Thompson, A. A. Jammal, S. I. Berchuck, E. B. Mariottoni, and 665 F. A. Medeiros, ‘‘Assessment of a segmentation-free deep
learning algo- 666 rithm for diagnosing glaucoma from optical coherence tomography scans,’’ 667 JAMA Ophthalmol., vol. 138, no.
4, pp. 333–339, Apr. 2020.
10. Smith, et al, "CNN-Based Approaches for Glaucoma Screening in Diverse Populations." Journal of Glaucoma, vol. 32, no. 5, pp. 389– 397, 2023.
1132, 2023.
2. C. Bowd, A. Belghith, L. M. Zangwill, M. Christopher, M. H. Goldbaum, 685 R. Fan, J. Rezapour, S. Moghimi, A. Kamalipour, H.
Hou, and 686 R. N. Weinreb, ‘‘Deep learning image analysis of optical coherence tomography angiography measured vessel density
improves classification of healthy and glaucoma eyes,’’ Amer. J. Ophthalmol., vol. 236, pp. 298–308, 689 Apr. 2022.
3. R. Ali, et al, "A Novel Convolutional Neural Network Approach for Early Detection of Glaucoma Using OCT Images." IEEE
Transactions on Biomedical Engineering, vol. 70, no. 4, pp. 987–994, 2023
4. T. Wang, et al, "Enhancing Glaucoma Detection Using Advanced CNN Techniques with Multi-modal Imaging." American Journal of
Ophthalmology, vol. 240, pp. 102–110, 2024.
5. A. Kamalipour, S. Moghimi, H. Hou, R. C. Penteado, W. H. Oh, 710 J. A. Proudfoot, N. El-Nimri, E. Ekici, J. Rezapour, L. M. Zangwill,
711 C. Bowd, and R. N. Weinreb, ‘‘OCT angiography artifacts in glaucoma,’’ 712 Ophthalmology, vol. 128, no. 10, pp. 1426–1437,
Oct. 2021.
6. Harris, G. Guidoboni, B. Siesky, S. Mathew, A. C. Verticchio Vercellin, 570 L. Rowe, and J. Arciero, ‘‘Ocular blood flow as a clinical
observation: 571 Value, limitations and data analysis,’’ Prog. Retinal Eye Res., vol. 78, 572 Sep. 2020, Art. no. 100841.
7. K. Liu, et al, "Multimodal Deep Learning for Enhanced Glaucoma Detection: Combining Fundus and OCT Images." IEEE
Transactions on Medical Imaging, vol. 43, no. 2, pp. 300–309, 2024.
8. J. Doe, et al, "Application of Convolutional Neural Networks in Detecting Glaucoma from OCT Images." Ophthalmic Surgery, Lasers
& Imaging Retina, vol. 54, no. 2, pp. 123–130, 2023.
9. A. C. Thompson, A. A. Jammal, S. I. Berchuck, E. B. Mariottoni, and 665 F. A. Medeiros, ‘‘Assessment of a segmentation-free deep
learning algo- 666 rithm for diagnosing glaucoma from optical coherence tomography scans,’’ 667 JAMA Ophthalmol., vol. 138, no.
4, pp. 333–339, Apr. 2020.
10. Smith, et al, "CNN-Based Approaches for Glaucoma Screening in Diverse Populations." Journal of Glaucoma, vol. 32, no. 5, pp. 389– 397, 2023.
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