Lin X, Yufeng X, Pan X, Jingya X, Ding Y, Sun X, Song X, Ren Y, Shan PF. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Sci Rep. 2020;10(1):1–11. https://doi.org/10.1038/s41598-020-71908-9

Article  CAS  Google Scholar 

Hemalakshmi GR, Santhi D, Mani VRS, et al. Classification of retinal fundus image using MS-DRLBP features and CNN-RBF classifier. J Ambient Intell Human Comput. 2021;12:8747–62. https://doi.org/10.1007/s12652-020-02647-y.

Article  Google Scholar 

Jozsa F, Hall WA. Neuroanatomy, Retina. [Updated 2026 Feb 22]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2026 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK545310/

Khalifa NEM, Loey M, Taha MHN, Mohamed HNET. Deep transfer learning models for medical diabetic retinopathy detection. Acta Inform Med. 2019;27(5):327–32. https://doi.org/10.5455/aim.2019.27.327-332.

Article  PubMed  PubMed Central  Google Scholar 

Gundluru N, Rajput DS, Lakshmanna K, Kaluri R, Shorfuzzaman M, Uddin M, et al. Enhancement of detection of diabetic retinopathy using Harris hawks optimization with deep learning model. Comput Intell Neurosci. 2022;8512469. https://doi.org/10.1155/2022/8512469.

Article  PubMed  PubMed Central  Google Scholar 

Lee MH, Chin YH, Ng CH, Nistala KRY, Ow ZGW, Sundar G, et al. Risk factors of thyroid eye disease. Endocr Pract. 2021;27(3):245–53. https://doi.org/10.1016/j.eprac.2020.11.011

Article  PubMed  Google Scholar 

Arslan G, Erdaş ÇB. Detection of cataract, diabetic retinopathy and glaucoma eye diseases with deep learning approach. Intell Methods Eng Sci. 2023;2(2):42–7. https://doi.org/10.58190/imiens.2023.11

Article  Google Scholar 

Kumar P, Gupta M. Optical coherence tomography image-based eye disease detection using deep convolutional neural network. Health Inf Sci Syst. 2022;10(1):13. https://doi.org/10.1007/s13755-022-00182-y

Article  PubMed  PubMed Central  Google Scholar 

Korot E, Gonçalves MB, Huemer J, Beqiri S, Khalid H, Kelly M, et al. Clinician-driven AI: code-free self-training on public data for diabetic retinopathy referral. JAMA Ophthalmol. 2023;141(11):1029–36. https://doi.org/10.1001/jamaophthalmol.2023.4508

Article  PubMed  PubMed Central  Google Scholar 

Lalithadevi B, Krishnaveni S. Diabetic retinopathy detection and severity classification using optimized deep learning with explainable AI technique. Multimed Tools Appl. 2024;83:89949–90013. https://doi.org/10.1007/s11042-024-18863-z.

Article  Google Scholar 

Mary A, Kavitha P. Diabetic retinopathy disease detection using Shapley additive ensembled DenseNet-121 ResNet-50 model. Multimed Tools Appl. 2024;83:69797–824. https://doi.org/10.1007/s11042-024-18309-6.

Article  Google Scholar 

J P, B VK. An ensemble approach for classification of diabetic retinopathy in fundus image. Multimed Tools Appl. 2024. https://doi.org/10.1007/s11042-024-19353-y.

Article  Google Scholar 

Saproo D, Mahajan AN, Narwal S. Deep learning based binary classification of diabetic retinopathy images using transfer learning approach. J Diabetes Metab Disord. 2024;23:2289–314. https://doi.org/10.1007/s40200-024-01497-1.

Article  PubMed  PubMed Central  Google Scholar 

Baba SM, Bala I, Dhiman G, et al. Automated diabetic retinopathy severity grading using novel DR-ResNet + deep learning model. Multimed Tools Appl. 2024;83:71789–831. https://doi.org/10.1007/s11042-024-18434-2.

Article  Google Scholar 

Arora L, Singh SK, Kumar S, et al. Ensemble deep learning and EfficientNet for accurate diagnosis of diabetic retinopathy. Sci Rep. 2024;14:30554. https://doi.org/10.1038/s41598-024-81132-4.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kavitha NS, Kasthuri N. An efficient automatic diabetic retinopathy grading using a two-way cascaded convolution neural network. Discover Computing. 2025;28:7. https://doi.org/10.1007/s10791-024-09486-x.

Article  Google Scholar 

Liu K, Si T, Huang C, et al. Diagnosis and detection of diabetic retinopathy based on transfer learning. Multimed Tools Appl. 2024;83:82945–61. https://doi.org/10.1007/s11042-024-18792-x.

Article  Google Scholar 

El Yadari FZ, Chougrad H, Idrissi Khamlichi Y. Multi-class diabetic retinopathy classification using transfer learning and MixUp data augmentation. In: Serrhini M, Ghoumid K, editors. Advances in Smart Medical, IoT & Artificial Intelligence. ICSMAI 2024. Cham: Springer; 2024. pp. 1–12. https://doi.org/10.1007/978-3-031-66850-0_34.

Chapter  Google Scholar 

Bhimavarapu U. Improved dynamic threshold based fuzzy logic to detect and classify Diabetic Retinopathy. Multimed Tools Appl. 2025;84:3117–35. https://doi.org/10.1007/s11042-024-20567-3.

Article  Google Scholar 

Vijay K, Krithiga P, Kavirakesh S, Swetha S, Vishal B. Early detection of diabetic retinopathy using deep convolutional neural network. In: Geetha R, Dao NN, Khalid S, editors. Advances in Artificial Intelligence and Machine Learning in Big Data Processing. AAIMB 2023. Cham: Springer; 2025. pp. 1–12. https://doi.org/10.1007/978-3-031-73065-8_26.

Chapter  Google Scholar 

Modaresnia Y, Abedinzadeh Torghabeh F, Hosseini SA. Enhancing multi-class Diabetic Retinopathy detection using tuned hyper-parameters and modified deep transfer learning. Multimed Tools Appl. 2024;83:81455–76. https://doi.org/10.1007/s11042-024-18506-3.

Article  Google Scholar 

Sunil SS, Vindhya AS. Efficient Diabetic Retinopathy classification grading using GAN based EM and PCA learning framework. Multimed Tools Appl. 2025;84:5311–34. https://doi.org/10.1007/s11042-024-18553-w.

Article  Google Scholar 

Akhtar S, Aftab S, Ali O, et al. A deep learning based model for Diabetic Retinopathy grading. Sci Rep. 2025;15:3763. https://doi.org/10.1038/s41598-025-87171-9.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kapoor P, Arora S. Optic-GAN: a generalized data augmentation model to enhance the Diabetic Retinopathy detection. Int J Inf Technol. 2025. https://doi.org/10.1007/s41870-025-02426-y.

Article  Google Scholar 

Jindal H, Jain S, Aggarwal A. Ensemble Diabetic Retinopathy detection in 2-D color fundus retina scan. Wirel Pers Commun. 2024;137:1469–90. https://doi.org/10.1007/s11277-024-11363-1.

Article  Google Scholar 

Mondal SS, Mandal N, Singh KK. Precise detection of Diabetic Retinopathy using adaptive remora optimization algorithm with deep adversarial approach. Multimed Tools Appl. 2024. https://doi.org/10.1007/s11042-024-19776-7.

Article  Google Scholar 

Priya AAM. A novel deep learning approach for diabetic retinopathy classification using optical coherence tomography angiography. Multimed Tools Appl. 2025. https://doi.org/10.1007/s11042-025-20708-2.

Article  Google Scholar 

Gargi M, Eluri RK, Samantray OP, Hajarathaiah K. Compact pyramidal dense mixed attention network for diabetic retinopathy severity prediction under deep learning. Biomed Signal Process Control. 2025;100:106960. https://doi.org/10.1016/j.bspc.2024.106960

Article  Google Scholar 

Asim BlueMoon. DiabeticRetinopathy_Messidor_EyePACS_Preprocessed. Kaggle. https://www.kaggle.com/datasets/mohammadasimbluemoon/diabeticretinopathy-messidor-eyepac-preprocessed

APTOS 2019 Blindness Detection. Kaggle. https://www.kaggle.com/competitions/aptos2019-blindness-detection

Mutawa AM, Alnajdi S, Sruthi S. Transfer learning for diabetic retinopathy detection: A study of dataset combination and model performance. Appl Sci Basel. 2023;13:5685. https://doi.org/10.3390/app13095685.

Article  CAS  Google Scholar 

Kallel F, Echtioui A. Retinal fundus image classification for diabetic retinopathy using transfer learning technique. Signal Image Video Process. 2024;18:1143–53. https://doi.org/10.1007/s11760-023-02820-8.

Article  Google Scholar