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Deep learning of fundus images and optical coherence tomography images for ocular disease detection – a review

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Abstract

Deep Learning (DL) has proliferated interest in ocular disease detection in recent years, and several DL architectures were proposed. DL architectures deploy multiple layers to capture features in fundus images and ocular computed tomography images which in turn are used for the classification of images or segmentation of regions-of-interest in images. Notable among them are convolutional neural networks, recurrent neural networks, generative adversarial networks for classification, U-Net and Y-Net for segmentation, and transformer-based approaches for DR detection. Existing review articles focus either on one type of disease (say, diabetic retinopathy (DR) or glaucoma) or on one type of deep learning task (say, classification or segmentation). This article presents a detailed survey of DL architectures for detecting ocular diseases from various ocular image types, covering a variety of DL tasks. In addition to baseline approaches, several variants of them are also presented as they were shown to outperform their baseline counterpart. This review covers a wide range of applications including DR classification, DR grading, glaucoma detection, retinal vessel segmentation, optic disc segmentation, and optic cup segmentation.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. https://www.nei.nih.gov/learn-about-eye-health/eye-conditions-and-diseasesAccessed 24 July 2023

  2. https://www.mayoclinic.org/diseases-conditions/glaucoma/symptoms-causes/syc-20372839. Accessed 24 Jul 2023

  3. Ouda O, AbdelMaksoud E, Abd El-Aziz AA, Mohammed E (2022) Multiple ocular disease diagnosis using fundus images based on multi-label deep learning classification. Electronics (Switzerland) 11(13):1966. https://doi.org/10.3390/electronics11131966

  4. https://www.aao.org/eye-health/anatomy/fundus. Accessed 08 Dec 2023

  5. https://www.opsweb.org/page/fundusphotography. Accessed 08 Dec 2023

  6. Sengupta S, Singh A, Leopold HA, Gulati T, Lakshminarayanan V (2020) Ophthalmic diagnosis using deep learning with fundus images – A critical review. Artificial Intelligence in Medicine, 102:101758, ISSN 0933–3657. https://doi.org/10.1016/j.artmed.2019.101758

  7. Leopold HA, Zelek JS, Lakshminarayanan V. Sejdi¢ E, Falk TH, editors. (2018) Deep learning methods for retinal image analysis in signal processing and machine learning for biomedical big data. Boca Raton, FL: CRC Press; 329–65

  8. Xiao Z, Zhang X, Lei G, Zhang F, Wu J, Tong J, Ogunbona P, Shan C (2017) Automatic non-proliferative diabetic retinopathy screening system based on color fundus image. BioMed Eng OnLine https://doi.org/10.1186/s12938-017-0414-z

  9. https://glaucoma.org/optic-nerve-cupping. Accessed 08 Dec 2023. Article by John S. Cohen, MD and Harry A. Quigley, MD. Last reviewed March 16, 2022

  10. https://my.clevelandclinic.org/health/diagnostics/17293-optical-coherence-tomography. Accessed 08 Dec 2023

  11. https://www.northshore.org/healthresources/encyclopedia/encyclopedia.aspx?DocumentHwid=hw209926#:~:text=CT%20scans%20of%20the%20eyes,around%20the%20nose%20(sinuses. Accessed 08 Dec 2023

  12. https://www.oct-optovue.com/oct-retina/oct-diabete/files/oct-diabetic-retina.jpg [Last accessed: 08-Dec-2023]

  13. Umezurike B, Akhimien M, Udeala O, Green U, Agbo O, Ohaeri M (2019) Primary Open Angle Glaucoma: The Pathophysiolgy, Mechanisms, Future Diagnostic and Therapeutic Directions. Ophthalmology Research: An International Journal. 1–17. https://doi.org/10.9734/or/2019/v10i330106. https://doi.org/10.9734/or/2019/v10i330106

  14. Susrutha S, Mathur R (2023) Review on ocular disease recognition using deep learning. In 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT). (2023) 316–321 2023. 979–8–3503–9648–5/23/$31.00 ©2023 IEEE. https://doi.org/10.1109/InCACCT57535.2023.10141747

  15. Ran et al (2021) Deep learning in glaucoma with optical coherence tomography: a review. Eye 2021(35):188–201. https://doi.org/10.1038/s41433-020-01191-5

    Article  Google Scholar 

  16. https://developer.ibm.com/articles/cc-machine-learning-deep-learning-architectures/. Accessed 24 July 2023

  17. https://hub.packtpub.com/top-5-deep-learning-architectures/. Accessed 24 Jul 2023

  18. https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/. Accessed 24 Jul 2023

  19. Mehta S, Mercan E, Bartlett J, Weaver D, Elmore JG, Shapiro L (n.d.) Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images. https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm552742.htm

  20. Shankar K et al (2020) Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. Pattern Recogn Lett 133:210–216. https://doi.org/10.1016/j.patrec.2020.02.0260167-8655/©2020ElsevierB.V

    Article  Google Scholar 

  21. Ramchandre S et al. (2020) A deep learning approach for diabetic retinopathy detection using transfer learning. In 2020 IEEE International Conference for Innovation in Technology, INOCON 2020, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/INOCON50539.2020.9298201

  22. Chen W, Yang B, Li J, Wang J (2020) An approach to detecting diabetic retinopathy based on integrated shallow convolutional neural networks. IEEE Access 8: 178552–62. Digital Object Identifier https://doi.org/10.1109/ACCESS.2020.3027794

  23. Wang J, Bai Y, Xia B (2020) Simultaneous diagnosis of severity and features of diabetic retinopathy in fundus photography using deep learning. IEEE J Biomed Health Inform 24(12): 3397–3407. Digital Object Identifier https://doi.org/10.1109/JBHI.2020.3012547

  24. Qiao L, Zhu Y, Zhou H (2020) Diabetic retinopathy detection using prognosis of microaneurysm and early diagnosis system for non-proliferative diabetic retinopathy based on deep learning algorithms. IEEE Access 8: 104292–302. Digital Object Identifier. https://doi.org/10.1109/ACCESS.2020.2993937

  25. Zhang G et al. (2021) Hybrid graph convolutional network for semi-supervised retinal image classification. IEEE Access 9: 35778–89. Digital Object Identifier https://doi.org/10.1109/ACCESS.2021.3061690

  26. Liu T et al. (2021) A novel diabetic retinopathy detection approach based on deep symmetric convolutional neural network. IEEE Access. Digital Object Identifier https://doi.org/10.1109/ACCESS.2021.3131630

  27. Garifullin A, Lensu L, Uusitalo H (2021) Deep bayesian baseline for segmenting diabetic retinopathy lesions: advances and challenges. Comput Biol Med 136. https://doi.org/10.1016/j.compbiomed.2021.104725

  28. Hua CH et al (2021) Convolutional network with twofold feature augmentation for diabetic retinopathy recognition from multi-modal images. IEEE J Biomed Health Inform 25(7):2686–2697. https://doi.org/10.1109/JBHI.2020.3041848

    Article  Google Scholar 

  29. Das S et al. (2021) Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy. Biomed Signal Process Control 68. https://doi.org/10.1016/j.bspc.2021.102600

  30. Xu Y, Fan Y (2022) Dual-channel asymmetric convolutional neural network for an efficient retinal blood vessel segmentation in eye fundus images. Biocybern Biomed Eng 42(2):695–706. https://doi.org/10.1016/j.bbe.2022.05.003

    Article  Google Scholar 

  31. Padmanayana, Anoop A (2022) Binary classification of DR-Diabetic retinopathy using CNN with fundus colour images. Materials Today: Proceedings 58:212–216. https://doi.org/10.1016/j.matpr.2022.01.466

    Article  Google Scholar 

  32. Arsalan M, Haider A, Lee YW, Park KR (2022) Detecting retinal vasculature as a key biomarker for deep learning-based intelligent screening and analysis of diabetic and hypertensive retinopathy. Expert Syst Appl 200. https://doi.org/10.1016/j.eswa.2022.117009

  33. Listyalina L, Utari EL, Puspaningtyas DE, Dharmawan DA (2022) Fovea and diabetic retinopathy: understanding the relationship using a deep interpretable classifier. Comput Meth Prog Biomed Update 2:100059. https://doi.org/10.1016/j.cmpbup.2022.100059

    Article  Google Scholar 

  34. Farag MM, Fouad M, Abdel-Hamid AT (2022) Automatic severity classification of diabetic retinopathy based on denseNet and convolutional block attention module. IEEE Access 10: 38299–308. Digital Object Identifier https://doi.org/10.1109/ACCESS.2022.3165193

  35. Bhati A, Gour N, Khanna P, Ojha A (2023) Discriminative kernel convolution network for multi-Label ophthalmic disease detection on imbalanced fundus image dataset. Comput Biol Med 153. https://doi.org/10.1016/j.compbiomed.2022.106519

  36. Nayak DR et al. (2021) ECNet: An evolutionary convolutional network for automated glaucoma detection using fundus images. Biomed Signal Process Control 67. https://doi.org/10.1016/j.bspc.2021.102559

  37. Tymchenko B, Marchenko P, Spodarets D (2020) Deep learning approach to diabetic retinopathy detection. http://arxiv.org/abs/2003.02261. https://doi.org/10.48550/arXiv.2003.02261

  38. Ghazal M et al. (2020) Accurate detection of non-proliferative diabetic retinopathy in optical coherence tomography images using convolutional neural networks. IEEE Access 8: 34387–97. Digital Object Identifier https://doi.org/10.1109/ACCESS.2020.2974158

  39. Md. Robiul Islam et al. (2020) Transfer learning based diabetic retinopathy detection with a novel preprocessed layer. 2020 IEEE Region 10 Symposium (TENSYMP), 5–7 June 2020, Dhaka, Bangladesh. 978–1–7281–7366–5/20/$31.00 ©2020 IEEE. https://doi.org/10.1109/TENSYMP50017.2020.9230648

  40. Saeed F, Hussain M, Aboalsamh HA (2021) Automatic diabetic retinopathy diagnosis using adaptive fine-tuned convolutional neural network. IEEE Access 9: 41344–59. Digital Object Identifier https://doi.org/10.1109/ACCESS.2021.3065273

  41. Martinez-Murcia FJ et al (2021) Deep residual transfer learning for automatic diagnosis and grading of diabetic retinopathy. Neurocomputing 452:424–434. https://doi.org/10.1016/j.neucom.2020.04.148

    Article  Google Scholar 

  42. Islam, Md Robiul et al. (2022) Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images. Comput Biol Med 146. https://doi.org/10.1016/j.compbiomed.2022.105602

  43. Tang MCS, Teoh SS, Ibrahim H, Embong Z (2022) A deep learning approach for the detection of neovascularization in fundus images using transfer learning. IEEE Access 10: 20247–58. Digital Object Identifier https://doi.org/10.1109/ACCESS.2022.3151644

  44. Yang B et al. (2022) Classification of Diabetic Retinopathy Severity Based on GCA Attention Mechanism. IEEE Access 10: 2729–39. Digital Object Identifier https://doi.org/10.1109/ACCESS.2021.3139129

  45. Wang X et al. (2020) UD-MIL: Uncertainty-Driven Deep Multiple Instance Learning for OCT Image Classification. IEEE J Biomed Health Inform 24(12): 3431–42. Digital Object Identifier https://doi.org/10.1109/JBHI.2020.2983730

  46. Deepa V, Sathish Kumar C, Cherian T (2022) Ensemble of Multi-Stage Deep Convolutional Neural Networks for Automated Grading of Diabetic Retinopathy Using Image Patches. J King Saud Univ Comput Inform Sci 34(8):6255–6265. https://doi.org/10.1016/j.jksuci.2021.05.009

    Article  Google Scholar 

  47. Kaushik H et al. (2021) Diabetic Retinopathy Diagnosis from Fundus Images Using Stacked Generalization of Deep Models. IEEE Access 9: 108276–92. Digital Object Identifier https://doi.org/10.1109/ACCESS.2021.3101142

  48. Fang L, Qiao H (2022) Diabetic Retinopathy Classification Using a Novel DAG Network Based on Multi-Feature of Fundus Images. Biomed Signal Process Control 77. https://doi.org/10.1016/j.bspc.2022.103810

  49. Wang X et al (2022) Joint Learning of Multi-Level Tasks for Diabetic Retinopathy Grading on Low-Resolution Fundus Images. IEEE J Biomed Health Inform 26(5):2216–2227. https://doi.org/10.1109/JBHI.2021.3119519

    Article  Google Scholar 

  50. Roychowdhury S, Koozekanani DD, Parhi KK (2014) DREAM: Diabetic Retinopathy Analysis Using Machine Learning. IEEE J Biomed Health Inform 18(5):1717–1728. https://doi.org/10.1109/JBHI.2013.2294635

    Article  Google Scholar 

  51. Zhou Y, Wang B, He X, Cui S, Shao L (2019) DR-GAN: Conditional Generative Adversarial Network for Fine-Grained Lesion Synthesis on Diabetic Retinopathy Images. https://doi.org/10.1109/JBHI.2020.3045475

  52. Park KB, Choi SH, Lee JY (2020) M-GAN: Retinal Blood Vessel Segmentation by Balancing Losses through Stacked Deep Fully Convolutional Networks. IEEE Access 8:146308–146322. https://doi.org/10.1109/ACCESS.2020.3015108

    Article  Google Scholar 

  53. Rodrigues EO, Conci A, Liatsis P (2020) ELEMENT: Multi-Modal Retinal Vessel Segmentation Based on a Coupled Region Growing and Machine Learning Approach. IEEE J Biomed Health Inform 24(12):3507–3519. https://doi.org/10.1109/JBHI.2020.2999257

    Article  Google Scholar 

  54. Chen Y, Long J, Guo J (2021) RF-GANs: A Method to Synthesize Retinal Fundus Images Based on Generative Adversarial Network. Computational Intelligence and Neuroscience, 2021. https://doi.org/10.1155/2021/3812865

  55. Wang S et al (2021) Diabetic retinopathy diagnosis using multichannel generative adversarial network with semisupervision. IEEE Trans Autom Sci Eng 18(2):574–585. https://doi.org/10.1109/TASE.2020.2981637

    Article  Google Scholar 

  56. Abdelsalam MM, Zahran MA (2021) A novel approach of diabetic retinopathy early detection based on multifractal geometry analysis for OCTA macular images using support vector machine. IEEE Access 9: 22844–58. Digital Object Identifier https://doi.org/10.1109/ACCESS.2021.3054743

  57. Li Y et al. (2021) Semi-supervised auto-encoder graph network for diabetic retinopathy grading. IEEE Access 9: 140759–67. Digital Object Identifier https://doi.org/10.1109/ACCESS.2021.3119434

  58. Abbood SH, Hamed HNA, Rahim MSM, Alaidi AHM, ALRikabi HTS (2022) DR-LL Gan: Diabetic Retinopathy Lesions Synthesis using Generative Adversarial Network. Intl J Online Biomed Eng 18(3):151–163. https://doi.org/10.3991/ijoe.v18i03.28005

    Article  Google Scholar 

  59. Sivapriya G et al (2022) Segmentation of hard exudates for the detection of diabetic retinopathy with RNN based semantic features using fundus images. Materials Today: Proceedings 64:693–701. https://doi.org/10.1016/j.matpr.2022.05.189

    Article  Google Scholar 

  60. Zhao L, Chi H, Zhong T, Jia Y (2023) Perception-oriented generative adversarial network for retinal fundus image super-resolution. Comput Biol Med, 107708. https://doi.org/10.1016/j.compbiomed.2023.107708

  61. Xiang D, Yan S, Guan Y, Cai M, Li Z, Liu H, Chen X, Tian B (2023) Semi-supervised dual stream segmentation network for fundus lesion segmentation. IEEE Trans Med Imaging 42(3):713–725. https://doi.org/10.1109/TMI.2022.3215580

    Article  Google Scholar 

  62. Guo S (2021) Fundus image segmentation via hierarchical feature learning. Comput Biol Med 138. https://doi.org/10.1016/j.compbiomed.2021.104928

  63. Zhang C, Lei T, Chen P (2022) Diabetic retinopathy grading by a source-free transfer learning approach. Biomed Signal Process Control 73. https://doi.org/10.1016/j.bspc.2021.103423

  64. Cao P et al. (2022) Collaborative learning of weakly-supervised domain adaptation for diabetic retinopathy grading on retinal images. Comput Biol Med 144. https://doi.org/10.1016/j.compbiomed.2022.105341

  65. Luo L, Chen D, Xue D (2018) Retinal blood vessels semantic segmentation method based on modified U-Net. In Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018, Institute of Electrical and Electronics Engineers Inc., 1892–95. https://doi.org.egateway.chennai.vit.ac.in/https://doi.org/10.1109/CCDC.2018.8407435

  66. Hu J et al. (2019) S-UNet: A Bridge-Style U-Net Framework with a Saliency Mechanism for Retinal Vessel Segmentation. IEEE Access 7: 174167–77. Digital Object Identifier https://doi.org/10.1109/ACCESS.2019.2940476

  67. Alsaih K, Yusoff MZ, Tang TB, Faye I, Mériaudeau F (2020) Deep learning architectures analysis for age-related macular degeneration segmentation on optical coherence tomography scans. Comput Meth Progr Biomed, 195. https://doi.org/10.1016/j.cmpb.2020.105566

  68. Lv Y, Ma H, Li J, Liu S (2020) Attention Guided U-Net with Atrous Convolution for Accurate Retinal Vessels Segmentation. IEEE Access 8: 32826–39. Digital Object Identifier https://doi.org/10.1109/ACCESS.2020.2974027

  69. Guo X et al. (2020) Retinal Vessel Segmentation Combined with Generative Adversarial Networks and Dense U-Net. IEEE Access 8: 194551–60. Digital Object Identifier https://doi.org/10.1109/ACCESS.2020.3033273

  70. Zong Y et al. (2020) U-Net Based Method for Automatic Hard Exudates Segmentation in Fundus Images Using Inception Module and Residual Connection. IEEE Access 8: 167225–35. Digital Object Identifier https://doi.org/10.1109/ACCESS.2020.3023273

  71. Yuan Y, Zhang L, Wang L, Huang H (2022) Multi-Level Attention Network for Retinal Vessel Segmentation. IEEE J Biomed Health Inform 26(1): 312–23. Digital Object Identifier https://doi.org/10.1109/JBHI.2021.3089201

  72. Abdelmaksoud E et al. (2021) Automatic Diabetic Retinopathy Grading System Based on Detecting Multiple Retinal Lesions. IEEE Access 9: 15939–60. Digital Object Identifier https://doi.org/10.1109/ACCESS.2021.3052870

  73. Gegundez-Arias ME, Marin-Santos D, Perez-Borrero I, Vasallo-Vazquez MJ (2021) A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net Model. Comput Meth Prog Biomed 205. https://doi.org/10.1016/j.cmpb.2021.106081

  74. Kundu S et al (2022) Nested U-Net for segmentation of red lesions in retinal fundus images and sub-image classification for removal of false positives. J Digit Imaging 35(5):1111–1119. https://doi.org/10.1007/s10278-022-00629-4

    Article  Google Scholar 

  75. Wang B et al. (2021) CSU-Net: A Context Spatial U-Net for Accurate Blood Vessel Segmentation in Fundus Images. IEEE J Biom Health Inform 25(4): 1128–38. Digital Object Identifier https://doi.org/10.1109/JBHI.2020.3011178

  76. Agrawal R et al (2022) Deep dive in retinal fundus image segmentation using deep learning for retinopathy of prematurity. Multimed Tools Appl 81(8):11441–11460. https://doi.org/10.1007/s11042-022-12396-z

    Article  Google Scholar 

  77. Wei J et al (2022) Genetic U-Net: Automatically Designed Deep Networks for Retinal Vessel Segmentation Using a Genetic Algorithm. IEEE Trans Med Imaging 41(2):292–307. https://doi.org/10.1109/TMI.2021.3111679

    Article  Google Scholar 

  78. Liu Y et al. (2023) Wave-Net: A Lightweight Deep Network for Retinal Vessel Segmentation from Fundus Images. Comput Biol Med 152. https://doi.org/10.1016/j.compbiomed.2022.106341

  79. Liu Y et al. (2022) ResDO-UNet: A Deep Residual Network for Accurate Retinal Vessel Segmentation from Fundus Images. Biomed Signal Process Control 79. https://doi.org/10.1016/j.bspc.2022.104087

  80. Ren K et al. (2022) An improved U-net based retinal vessel image segmentation method. Heliyon 8(10). https://doi.org/10.1016/j.heliyon.2022.e11187

  81. Li P, Liang L, Gao Z, Wang X (2023) AMD-Net: Automatic Subretinal Fluid and Hemorrhage Segmentation for Wet Age-Related Macular Degeneration in Ocular Fundus Images. Biomed Signal Process Control 80. https://doi.org/10.1016/j.bspc.2022.104262

  82. Li J, Gao G, Yang L, Liu Y (2023) GDF-Net: A Multi-Task Symmetrical Network for Retinal Vessel Segmentation. Biomed Signal Process Control 81. https://doi.org/10.1016/j.bspc.2022.104426

  83. Li X, Song J, Jiao W, Zheng Y (2023) MINet: multi-scale input network for fundus microvascular segmentation. Comput Biol Med 154. https://doi.org/10.1016/j.compbiomed.2023.106608

  84. Kou C, Li W, Yu Z, Yuan L (2020) An enhanced residual U-Net for microaneurysms and exudates segmentation in fundus images. IEEE Access 8: 185514–25. Digital Object Identifier https://doi.org/10.1109/ACCESS.2020.3029117

  85. Wang L et al. (2021) Automated segmentation of the optic disc from fundus images using an asymmetric deep learning network. Pattern Recognition 112. https://doi.org/10.1016/j.patcog.2020.107810

  86. Shanmugam P, Raja J, Pitchai R (2021) An automatic recognition of glaucoma in fundus images using deep learning and random forest classifier. Appl Soft Comput 109. https://doi.org/10.1016/j.asoc.2021.107512

  87. Shinde R (2021) Glaucoma detection in retinal fundus images using U-net and supervised machine learning algorithms. Intell-Based Med 5:100038. https://doi.org/10.1016/j.ibmed.2021.100038

    Article  Google Scholar 

  88. Sangeethaa SN (2023) Presumptive discerning of the severity level of glaucoma through clinical fundus images using hybrid polynet. Biomed Signal Process Control 81. https://doi.org/10.1016/j.bspc.2022.104347

  89. Haider A et al. (2022) Artificial intelligence-based computer-aided diagnosis of glaucoma using retinal fundus images. Expert Syst Appl 207. https://doi.org/10.1016/j.eswa.2022.117968

  90. Han J, Wang Y, Gong H (2022) Fundus retinal vessels image segmentation method based on improved U-Net. IRBM 43(6):628–639. https://doi.org/10.1016/j.irbm.2022.03.001

    Article  Google Scholar 

  91. Huang Z, Sun M, Liu Y, Wu J (2022) CSAUNet: A cascade self-Attention u-shaped network for precise fundus vessel segmentation. Biomed Signal Process Control 75. https://doi.org/10.1016/j.bspc.2022.103613

  92. Wang X et al (2023) CLC-Net: contextual and local collaborative network for lesion segmentation in diabetic retinopathy images. Neurocomputing 527:100–109. https://doi.org/10.1016/j.neucom.2023.01.013

    Article  Google Scholar 

  93. Zhang Y et al. (2021) TAU: Transferable Attention U-Net for Optic Disc and Cup Segmentation. Knowl-Based Syst 213. https://doi.org/10.1016/j.knosys.2020.106668

  94. Mallick S, Paul J, Sil J (2023) Response fusion attention U-ConvNext for accurate segmentation of optic disc and optic cup. Neurocomputing 559. https://doi.org/10.1016/j.neucom.2023.126798

  95. Roy A, Sharma K (2021) Retinal vessel detection using residual Y-Net. In 2021 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2021, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/SMARTGENCON51891.2021.9645749

  96. Geetha Pavani P, Biswal B, Gandhi TK (2023) Simultaneous multiclass retinal lesion segmentation using fully automated RILBP-YNet in diabetic retinopathy. Biomed Signal Process Control 86. https://doi.org/10.1016/j.bspc.2023.105205

  97. Farshad A, Yeganeh Y, Gehlbach P, Navab N (2022) Y-Net: A Spatiospectral Dual-Encoder Networkfor Medical Image Segmentation. http://arxiv.org/abs/2204.07613. https://doi.org/10.1007/978-3-031-16434-7_56

  98. Bhattacharya R et al. (2023) PY-Net: Rethinking Segmentation Frameworks with Dense Pyramidal Operations for Optic Disc and Cup Segmentation from Retinal Fundus Images. Biomed Signal Process Control 85. https://doi.org/10.1016/j.bspc.2023.104895

  99. Shen J, Hu Y, Zhang X, Gong Y, Kawasaki R, Liu J (2023) Structure-Oriented Transformer for retinal diseases grading from OCT images. Comput Biol Med, 152. https://doi.org/10.1016/j.compbiomed.2022.106445

  100. Xia X, Huang Z, Huang Z, Shu L, Li L (2022) A CNN-Transformer Hybrid Network for Joint Optic Cup and Optic Disc Segmentation in Fundus Images. Proceedings - 2022 International Conference on Computer Engineering and Artificial Intelligence, ICCEAI 2022, 482–486. https://doi.org/10.1109/ICCEAI55464.2022.00106

  101. Dai W, Mou C, Wu J, Ye X (2023) Diabetic Retinopathy Detection with Enhanced Vision Transformers: The Twins-PCPVT Solution. 2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information, ICETCI 2023, 403–407. https://doi.org/10.1109/ICETCI57876.2023.10176810

  102. Bi Q, Sun X, Yu S, Ma K, Bian C, Ning M, He N, Huang Y, Li Y, Liu H, Zheng Y (2023) MIL-ViT: A multiple instance vision transformer for fundus image classification. J Visual Commun Image Represent 97. https://doi.org/10.1016/j.jvcir.2023.103956

  103. Lian J, Liu T (2024) Lesion identification in fundus images via convolutional neural network-vision transformer. Biomed Signal Process Control 88. https://doi.org/10.1016/j.bspc.2023.105607

  104. Xu K, Huang S, Yang Z, Zhang Y, Fang Y, Zheng G, Lin B, Zhou M, Sun J (2023) Automatic detection and differential diagnosis of age-related macular degeneration from color fundus photographs using deep learning with hierarchical vision transformer. Comput Biol Med 167. https://doi.org/10.1016/j.compbiomed.2023.107616

  105. https://research.ibm.com/blog/what-is-federated-learningAccessed 26 Jul 2023

  106. https://link.springer.com/chapter/10.1007/978-981-13-9942-8_27. Accessed 26 Jul 2023

  107. https://www.techtarget.com/searchenterpriseai/definition/generative-AI. Accessed 26 July 2023

  108. https://generativeai.net/. Accessed 26 Jul 2023

  109. https://www.pnas.org/doi/abs/10.1073/pnas.1611835114. Accessed 26 Jul 2023

  110. Forgetting in Deep Learning - A study of techniques that are related to catastrophic forgetting in deep neural networks. https://towardsdatascience.com/forgetting-in-deep-learning-4672e8843a7f. Accessed 26 Jul 2023

  111. https://typeset.io/questions/what-are-the-challenges-of-using-vision-transformers-for-2bddj0e95a. Accessed 13 Dec 2023

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    Rizvana M & Sathiya Narayanan

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M, R., Narayanan, S. Deep learning of fundus images and optical coherence tomography images for ocular disease detection – a review. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18938-x

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