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Journals / Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi / 2026 / Cilt: 42 Sayı: 1

Diagnostic Methods and Deep Learning Applications in Eye Diseases: A Literature Review

Göz Hastalıklarında Tanı Yöntemleri ve Derin Öğrenme Uygulamaları: Bir Literatür Çalışması

Tayyip Özcan(Erciyes Üniversitesi)0000-0002-3111-5260
Hatice Türkdoğan(ERCİYES ÜNİVERSİTESİ)0000-0003-2112-825X
Fatih Horozoğlu(ERCİYES ÜNİVERSİTESİ)0000-0002-1442-6032
Pages
—
Publication Date
2026-03-17
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The study aims to summarize recent developments in the field and provide recommendations to guide future research.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The early detection of eye diseases is critically important for preventing vision loss and maintaining patients' quality of life.
📊
Key Findings & Data:In recent years, the rapid advancement of deep learning methods in biomedical imaging has found significant applications within the field of ophthalmology.
💡
Conclusion & Impact:The study aims to summarize recent developments in the field and provide recommendations to guide future research.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Göz Hastalıkları · Derin Öğrenme · Görüntü Analizi

Abstract

The early detection of eye diseases is critically important for preventing vision loss and maintaining patients' quality of life. In recent years, the rapid advancement of deep learning methods in biomedical imaging has found significant applications within the field of ophthalmology. Models developed using fundus photographs, optical coherence tomography, and retinal images have achieved high accuracy rates in the automated diagnosis of diabetic retinopathy, glaucoma, age-related macular degeneration, and other retina-based diseases. This review examines the current deep learning approaches used for detecting eye diseases, the datasets employed, architectural variations, and performance metrics. Convolutional neural networks, transfer learning, hybrid models, and, increasingly, vision transformer-based methods are comparatively evaluated. The study aims to summarize recent developments in the field and provide recommendations to guide future research.

Özet

Göz hastalıklarının erken teşhisi, görme kaybını önlemek ve hastaların yaşam kalitesini korumak açısından kritik öneme sahiptir. Son yıllarda, biyomedikal görüntülemede derin öğrenme yöntemlerindeki hızlı ilerleme, oftalmoloji alanında önemli uygulamalar bulmuştur. Fundus fotoğrafları, optik koherens tomografi ve retina görüntüleri kullanılarak geliştirilen modeller, diyabetik retinopati, glokom, yaşa bağlı makula dejenerasyonu ve diğer retina temelli hastalıkların otomatik teşhisinde yüksek doğruluk oranlarına ulaşmıştır. Bu derleme, göz hastalıklarını tespit etmek için kullanılan mevcut derin öğrenme yaklaşımlarını, kullanılan veri kümelerini, mimari farklılıkları ve performans ölçütlerini incelemektedir. Evrişimsel sinir ağları, transfer öğrenme, hibrit modeller ve giderek artan şekilde görü dönüştürücü tabanlı yöntemler karşılaştırmalı olarak değerlendirilmektedir. Çalışma, alandaki son gelişmeleri özetlemeyi ve gelecekteki araştırmalara yön verecek öneriler sunmayı amaçlamaktadır.

🔖

Cite This Article

Özcan, T., Türkdoğan, H., Horozoğlu, F. (2026). Diagnostic Methods and Deep Learning Applications in Eye Diseases: A Literature Review. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, Cilt: 42 Sayı: 1. https://doi.org/10.65520/erciyesfen.1770680

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References (58)

  1. Jain, L., Murthy, H. S., Patel, C., & Bansal, D. (2018, December). Retinal eye disease detection using deep learning. In 2018 Fourteenth International Conference on Information Processing (ICINPRO) (pp. 1-6). IEEE.
  2. Qummar, S., Khan, F. G., Shah, S., Khan, A., Shamshirband, S., Rehman, Z. U., & Jadoon, W. (2019). A deep learning ensemble approach for diabetic retinopathy detection. Ieee Access, 7, 150530-150539.
  3. Rajagopalan, N., Narasimhan, V., Kunnavakkam Vinjimoor, S., & Aiyer, J. (2021). RETRACTED ARTICLE: Deep CNN framework for retinal disease diagnosis using optical coherence tomography images. Journal of Ambient Intelligence and Humanized Computing, 12(7), 7569-7580.
  4. Li, Y., Xia, X., & Paulus, Y. M. (2018, April). Advances in retinal optical imaging. In Photonics (Vol. 5, No. 2, p. 9). MDPI.
  5. Yu, S., Lu, J., Cao, D., Liu, R., Liu, B., Li, T.,. & Lu, L. (2016). The role of optical coherence tomography angiography in fundus vascular abnormalities. BMC ophthalmology, 16(1), 107.
  6. Chalam, K. V., & Sambhav, K. (2016). Optical coherence tomography angiography in retinal diseases. Journal of ophthalmic & vision research, 11(1), 84.
  7. Staal, J., Abràmoff, M. D., Niemeijer, M., Viergever, M. A., & Van Ginneken, B. (2004). Ridge-based vessel segmentation in color images of the retina. IEEE transactions on medical imaging, 23(4), 501-509.
  8. Hoover, A. D., Kouznetsova, V., & Goldbaum, M. (2000). Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical imaging, 19(3), 203-210.
  9. Jordan, K. C., Menolotto, M., Bolster, N. M., Livingstone, I. A., & Giardini, M. E. (2017). A review of feature-based retinal image analysis. Expert Review of Ophthalmology, 12(3), 207-220.
  10. ‘‘DRIONS-DB Retinal Image Database — bob.db.drionsdb 1.0.1 documentation.” https://www.idiap.ch/software/bob/docs/bob/bob.db.drionsdb/master/index.html (Erişim Tarihi: 10.12.2024).
  11. ‘‘Messidor - ADCIS.” http://www.adcis.net/en/third-party/messidor/ (Erişim Tarihi: 11.09.2025).
  12. Alyoubi, W. L., Abulkhair, M. F., & Shalash, W. M. (2021). Diabetic retinopathy fundus image classification and lesions localization system using deep learning. Sensors, 21(11), 3704.
  13. Ibrahim, A., ‘‘OCT2017.” https://www.kaggle.com/datasets/namaneanasibrahim/oct2017 (Erişim Tarihi: 15.09.2024).
  14. Naren, O. S., ‘‘Retinal OCT Image Classification - C8. ” www.kaggle.com/datasets/obulisainaren/retinal-oct-c8 (Erişim Tarihi: 23.05.2025).
  15. Larxel, ‘‘Ocular Disease Recognition. ” https://www.kaggle.com/datasets/andrewmvd/ocular-disease-recognition-odir5k (Erişim Tarihi: 03.11.2024).
  16. Zhang, J., Dashtbozorg, B., Bekkers, E., Pluim, J. P., Duits, R., & ter Haar Romeny, B. M. (2016). Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores. IEEE transactions on medical imaging, 35(12), 2631-2644.
  17. Perez-Rovira, A., MacGillivray, T., Trucco, E., Chin, K. S., Zutis, K., Lupascu, C.,. & Dhillon, B. (2011, August). VAMPIRE: Vessel assessment and measurement platform for images of the REtina. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 3391-3394). IEEE.
  18. Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A.,. & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. jama, 316(22), 2402-2410.
  19. Elkholy, M., & Marzouk, M. A. (2024). Deep learning-based classification of eye diseases using Convolutional Neural Network for OCT images. Frontiers in Computer Science, 5, 1252295.
  20. Puneet, Kumar, R., & Gupta, M. (2022). Optical coherence tomography image based eye disease detection using deep convolutional neural network. Health Information Science and Systems, 10(1), 13.
  21. Kim, J., & Tran, L. (2021, October). Retinal disease classification from oct images using deep learning algorithms. In 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp. 1-6). Ieee.
  22. Hassan, E., Elmougy, S., Ibraheem, M. R., Hossain, M. S., AlMutib, K., Ghoneim, A.,. & Talaat, F. M. (2023). Enhanced deep learning model for classification of retinal optical coherence tomography images. Sensors, 23(12), 5393.
  23. Khan, A., Pin, K., Aziz, A., Han, J. W., & Nam, Y. (2023). Optical coherence tomography image classification using hybrid deep learning and ant colony optimization. Sensors, 23(15), 6706.
  24. Nazir, T., Nawaz, M., Rashid, J., Mahum, R., Masood, M., Mehmood, A.,. & Hussain, A. (2021). Detection of diabetic eye disease from retinal images using a deep learning based CenterNet model. Sensors, 21(16), 5283.
  25. Kadry, S., Rajinikanth, V., González Crespo, R., & Verdú, E. (2022). Automated detection of age-related macular degeneration using a pre-trained deep-learning scheme. The Journal of Supercomputing, 78(5), 7321-7340.
  26. Tayal, A., Gupta, J., Solanki, A., Bisht, K., Nayyar, A., & Masud, M. (2022). DL-CNN-based approach with image processing techniques for diagnosis of retinal diseases. Multimedia systems, 28(4), 1417-1438.
  27. Upadhyay, P. K., Rastogi, S., & Kumar, K. V. (2022). Coherent convolution neural network based retinal disease detection using optical coherence tomographic images. Journal of King Saud University-Computer and Information Sciences, 34(10), 9688-9695.
  28. Muthukannan, P. (2022). Optimized convolution neural network based multiple eye disease detection. Computers in Biology and Medicine, 146, 105648.
  29. Khan, U. S., & Khan, S. U. R. (2025). Boost diagnostic performance in retinal disease classification utilizing deep ensemble classifiers based on OCT. Multimedia Tools and Applications, 84(19), 21227-21247.
  30. Naik, G., Narvekar, N., Agarwal, D., Nandanwar, N., & Pande, H. (2024, March). Eye disease prediction using ensemble learning and attention on oct scans. In Future of Information and Communication Conference (pp. 21-36). Cham: Springer Nature Switzerland.
  31. Vardhan, K. B., Nidhish, M., & Shameem, D. N. (2024). Eye disease detection using deep learning models with transfer learning techniques. ICST Transactions on Scalable Information Systems, 11, 1-13.
  32. Dash, S. K., Sethy, P. K., Das, A., Jena, S., & Nanthaamornphong, A. (2024). Advancements in deep learning for automated diagnosis of ophthalmic diseases: a comprehensive review. IEEE Access.
  33. El-Ateif, S., & Idri, A. (2024). Eye diseases diagnosis using deep learning and multimodal medical eye imaging. Multimedia Tools and Applications, 83(10), 30773-30818.
  34. George, N., Shine, L., Abraham, B., & Ramachandran, S. (2024). A two-stage CNN model for the classification and severity analysis of retinal and choroidal diseases in OCT images. International Journal of Intelligent Networks, 5, 10-18.
  35. Aresta, G., Araújo, T., Schmidt-Erfurth, U., & Bogunović, H. (2025). Anomaly Detection in Retinal OCT Images With Deep Learning-Based Knowledge Distillation. Translational Vision Science & Technology, 14(3), 26-26.
  36. Daniel, D. A. P., Santhana Sagaya Mary, A., & Chidambaranathan, S. (2025). Deep Learning‐Based Neuro Computing to Classify and Diagnosis of Ophthalmology by OCT. Integrating Neurocomputing with Artificial Intelligence, 159-173.
  37. Mushtaq, A., Kumar, K. K., & Subhashini, R. (2025, July). Ocular disease recognition using deep learning. In AIP Conference Proceedings (Vol. 3257, No. 1, p. 020144). AIP Publishing LLC.
  38. Al-Fahdawi, S., Al-Waisy, A. S., Zeebaree, D. Q., Qahwaji, R., Natiq, H., Mohammed, M. A.,. & Deveci, M. (2024). Fundus-deepnet: Multi-label deep learning classification system for enhanced detection of multiple ocular diseases through data fusion of fundus images. Information Fusion, 102, 102059.
  39. Govindan, M., Dhakshnamurthy, V. K., Sreerangan, K., Nagarajan, M. D., & Rajamanickam, S. K. (2024). A framework for early detection of glaucoma in retinal fundus images using deep learning. Engineering Proceedings, 62(1), 3.
  40. Sharmin, S., Rashid, M. R., Khatun, T., Hasan, M. Z., & Uddin, M. S. (2024). A dataset of color fundus images for the detection and classification of eye diseases. Data in Brief, 57, 110979.
  41. Hussain, S. K., Ramay, S. A., Shaheer, H., Abbas, T., Mushtaq, M. A., Paracha, S., & Saeed, N. (2024). Automated classification of ophthalmic disorders using color fundus images. Volume, 12, 1344-1348.
  42. Tang, Q. Q., Yang, X. G., Wang, H. Q., Wu, D. W., & Zhang, M. X. (2024). Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus images. International Journal of Ophthalmology, 17(1), 188.
  43. Rom, Y., Aviv, R., Cohen, G. Y., Friedman, Y. E., Ianchulev, T., & Dvey-Aharon, Z. (2024). Diabetes detection from non-diabetic retinopathy fundus images using deep learning methodology. Heliyon, 10(16).
  44. Joshi, R. C., Sharma, A. K., & Dutta, M. K. (2024). VisionDeep-AI: Deep learning-based retinal blood vessels segmentation and multi-class classification framework for eye diagnosis. Biomedical Signal Processing and Control, 94, 106273.
  45. Patil, R., & Sharma, S. (2024). Automatic glaucoma detection from fundus images using transfer learning. Multimedia Tools and Applications, 83(32), 78207-78226.
  46. Patel, R. K., Chouhan, S. S., Lamkuche, H. S., & Pranjal, P. (2024). Glaucoma diagnosis from fundus images using modified Gauss-Kuzmin-distribution-based Gabor features in 2D-FAWT. Computers and Electrical Engineering, 119, 109538.
  47. Lenka, S., Mayaluri, Z. L., & Panda, G. (2025). Glaucoma detection from retinal fundus images using graph convolution based multi-task model. e-Prime-Advances in Electrical Engineering, Electronics and Energy, 11, 100931.
  48. Rayavel, P., Priyadharshini, S. A., Midhunadharshini, G., & Divyedharshini, G. (2025). Detection and classification of diabetic retinopathy in retinal fundus images using deep spiking Q Network optimized with partial reinforcement optimizer. Computers in Biology and Medicine, 196, 110863.
  49. Aghabeigi Alooghareh, M. M., Sheikhey, M. M., Sahafi, A., Pirnejad, H., & Naemi, A. (2025). Deep Learning for Comprehensive Analysis of Retinal Fundus Images: Detection of Systemic and Ocular Conditions. Bioengineering, 12(8), 840.
  50. Beegam, S., Kalra, M., Bhowmik, A., & Panda, J. K. (2025). Deep Learning Framework for Multiclass Detection of Ocular Diseases in Fundus Images.
  51. Priya, E., Dhinesh, M., Rohitkumar, G., & Dheena, M. (2025, May). Harnessing Deep Learning Models in Fundus Imaging for Early Diagnosis in Eye Disorders. In 2025 Fourth International Conference on Smart Technologies, Communication and Robotics (STCR) (pp. 1-5). IEEE.
  52. Ejaz, S., Zia, H. U., Majeed, F., Shafique, U., Altamiranda, S. C., Lipari, V., & Ashraf, I. (2025). Fundus image classification using feature concatenation for early diagnosis of retinal disease. Digital Health, 11, 20552076251328120.
  53. Yu, S., Lu, J., Cao, D., Liu, R., Liu, B., Li, T.,. & Lu, L. (2016). The role of optical coherence tomography angiography in fundus vascular abnormalities. BMC ophthalmology, 16(1), 107.
  54. Perez-Rovira, A., MacGillivray, T., Trucco, E., Chin, K. S., Zutis, K., Lupascu, C.,. & Dhillon, B. (2011, August). VAMPIRE: Vessel assessment and measurement platform for images of the REtina. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 3391-3394). IEEE.
  55. Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A.,. & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. jama, 316(22), 2402-2410.
  56. Hassan, E., Elmougy, S., Ibraheem, M. R., Hossain, M. S., AlMutib, K., Ghoneim, A.,. & Talaat, F. M. (2023). Enhanced deep learning model for classification of retinal optical coherence tomography images. Sensors, 23(12), 5393.
  57. Nazir, T., Nawaz, M., Rashid, J., Mahum, R., Masood, M., Mehmood, A.,. & Hussain, A. (2021). Detection of diabetic eye disease from retinal images using a deep learning based CenterNet model. Sensors, 21(16), 5283.
  58. Al-Fahdawi, S., Al-Waisy, A. S., Zeebaree, D. Q., Qahwaji, R., Natiq, H., Mohammed, M. A.,. & Deveci, M. (2024). Fundus-deepnet: Multi-label deep learning classification system for enhanced detection of multiple ocular diseases through data fusion of fundus images. Information Fusion, 102, 102059.