Teo ZL, Tham YC, Yu M, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis. Ophthalmology 2021;128:1580–91. doi: 10.1016/j.ophtha.2021.04.027.

He J, Cao T, Xu F, et al. Artificial intelligence-based screening for diabetic retinopathy at community hospital. Eye (Basingstoke) 2020;34:572–6. https://doi.org/10.1038/s41433-019-0562-4.

Bajwa A, Nosheen N, Talpur KI, Akram S. A prospective study on diabetic retinopathy detection based on modify convolutional neural network using fundus images at Sindh Institute of Ophthalmology & Visual Sciences. Diagnostics (Basel) 2023;13:393. doi: 10.3390/diagnostics13030393.

Vijayan M, Venkatakrishnan S. A regression-based approach to diabetic retinopathy diagnosis using Efficientnet. Diagnostics (Basel) 2023;13:774. doi: 10.3390/diagnostics13040774.

Ali G, Dastgir A, Iqbal MW, Anwar M, Faheem M. A hybrid convolutional neural network model for automatic diabetic retinopathy classification from fundus images. IEEE J Transl Eng Health Med 2023;11:341–50. doi: 10.1109/JTEHM.2023.3282104.

Arias-Serrano I, Velásquez-López PA, Avila-Briones LN, et al. Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB - retrained AlexNet convolutional neural network. F1000Res 2023;12:14. doi: 10.3906/elk-2105-36.

Nagasawa T, Tabuchi H, Masumoto H, et al. Accuracy of diabetic retinopathy staging with a deep convolutional neural network using ultra-wide-field fundus ophthalmoscopy and optical coherence tomography angiography. J Ophthalmol 2021;2021. doi: 10.1155/2021/6651175.

Parsa S, Khatibi T. Grading the severity of diabetic retinopathy using an ensemble of self-supervised pre-trained convolutional neural networks: ESSP-CNNs. Multimed Tools Appl 2024;1:34. doi: 10.1007/s11042-024-18968-5.

Ali MYS, Jabreel M, Valls A, Baget M, Abdel-Nasser M. LezioSeg: multi-scale attention affine-based CNN for segmenting diabetic retinopathy lesions in images. Electronics (Switzerland) 2023;12:4940. https://doi.org/10.3390/electronics12244940 .

Porwal P, Pachade S, Kamble R, et al. Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data 2018;3:25. https://doi.org/10.3390/data3030025.

Nadeem MW, Goh HG, Hussain M, Liew SY, Andonovic I, Khan MA. Deep learning for diabetic retinopathy analysis: a review, research challenges, and future directions. Sensors (Basel) 2022;22:6780. doi: 10.3390/s22186780.

Inamullah, Hassan S, Alrajeh NA, Mohammed EA, Khan S. Data diversity in convolutional neural network based ensemble model for diabetic retinopathy. Biomimetics (Basel) 2023;8:187. doi: 10.3390/biomimetics8020187.

Patil MS, Chickerur S, Kumar YVS, et al. Deep hyperparameter transfer learning for diabetic retinopathy classification. Turk J Electrical Eng Comput Sci 2021;29:2824–39. doi: 10.3906/elk-2105-36.

Abed MH, Muhammed LAN, Toman SH. Diabetic retinopathy diagnosis based on convolutional neural network. J Physics: Conference Series. IOP Publishing Ltd; 2021.

Kalpana Devi M, Mary Shanthi Rani M. Classification of diabetic retinopathy using ensemble of machine learning classifiers with IDRiD dataset. In: Suma V, Fernando X, Du KL, Wang H, editors. Evolutionary computing and mobile sustainable networks. Singapore: Springer Singapore;2022.pp.291–303. doi: 10.1088/1742-6596/1999/1/012117.

Pradhan A, Sarma B, Nath R, Das A, Chakraborty A. Diabetic retinopathy detection on retinal fundus images using convolutional neural network. In: Bhattacharjee A, Borgohain SK, Soni B, Verma G, Gao XZ. editors. Machine learning, image processing, network security and data sciences. MIND 2020. Communications in Computer and Information Science, vol 1240. Springer, Singapore. pp. 254–66. https://doi.org/10.1007/978-981-15-6315-7_21.

Indolia S, Goswami AK, Mishra SP, Asopa P. Conceptual understanding of convolutional neural network- a deep learning approach. In: Procedia Computer Science. Elsevier B.V.; 2018. pp. 679–88. https://doi.org/10.1016/j.procs.2018.05.069.

Taye MM. Theoretical understanding of convolutional neural network: concepts, architectures, applications, future directions. Computation 2023;11:52. https://doi.org/10.3390/computation11030052.

Raju M, Pagidimarri V, Barreto R, Kadam A, Kasivajjala V, Aswath A. Development of a deep learning algorithm for automatic diagnosis of diabetic retinopathy. Stud Health Technol Inform 2017;245:559-63.

Lin GM, Chen MJ, Yeh CH, et al. Transforming retinal photographs to entropy images in deep learning to improve automated detection for diabetic retinopathy. J Ophthalmol 2018;2018:2159702. doi: 10.1155/2018/2159702.

Grewal PS, Oloumi F, Rubin U, Tennant MTS. Deep learning in ophthalmology: a review. Can J Ophthalmol 2018;53:309-13. doi: 10.1016/j.jcjo.2018.04.019.