A Deep Learning–Driven CNN Framework for Early Identification of Retinal Disorders
International Journal of Medical Toxicology and Forensic Medicine,
Vol. 16 (2026),
1 January 2026
,
Page 1-6
https://doi.org/10.22037/ijmtfm.v16.51925
Abstract
Background: Early detection of retinal disorders is crucial to prevent vision loss. Traditional methods are time-consuming and prone to error. This study investigates a CNN-based deep learning framework for automating retinal disorder identification.
Methods: The study, conducted from January to April 2025 at the Department of Electronics and Communication Engineering, used publicly available retinal image datasets. The CNN model was trained on preprocessed retinal fundus images, with performance evaluated using accuracy, sensitivity, specificity, precision, recall, and F1-score.
Results: The proposed CNN framework exhibited excellent classification performance, achieving an overall accuracy of 94.6%, sensitivity of 93.2%, and specificity of 95.8%. The model successfully identified early-stage retinal abnormalities and demonstrated consistent results across various image samples. These findings suggest that the deep learning approach significantly outperforms conventional methods in early retinal disorder detection.
Conclusion: The CNN-based deep learning framework developed in this study provides a highly accurate and reliable tool for the early identification of retinal disorders. This approach has significant potential for integration into computer-aided diagnosis systems and tele-ophthalmology platforms, aiding early clinical decision-making and potentially preventing vision loss.
- Retinal disorders, Deep learning, Convolutional neural network, Fundus imaging, Early detection, Medical image analysis, Computer-Aided diagnosis
How to Cite
References
[1] Bhulakshmi D, Rajput DS. A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images. PeerJ Computer Science. 2024;10:e1947. [DOI:10.7717/peerj-cs.1947]
[2] Bellemo V, Lim G, Rim TH, Tan GSW, Cheung CY, Lee MLT, et al. Artificial intelligence screening for diabetic retinopathy: the real-world emerging application. Current Diabetes Reports. 2019;19:72. [DOI:10.1007/s11892-019-1189-3]
[3] Naz H, Ahuja NJ, Nijhawan R. Diabetic retinopathy detection using supervised and unsupervised deep learning: a review study. Artificial Intelligence Review. 2024;57:131. [DOI:10.1007/s10462-024-10770-x]
[4] Zhu S, Xiong C, Zhong Q, Yao Y. Diabetic Retinopathy Classification with Deep Learning via Fundus Images: A Short Survey. IEEE Access. 2024;12:20540-20558. [DOI:10.1109/ACCESS.2024.3361944]
[5] Hemanth SV, Alagarsamy S, Rajkumar TD. A novel deep learning model for diabetic retinopathy detection in retinal fundus images using pre-trained CNN and HWBLSTM. Journal of Biomolecular Structure and Dynamics. 2024. [DOI:10.1080/07391102.2024.2314269]
[6] Tovar-Arriaga S. Deep Learning Techniques for Retinal Layer Segmentation to Aid Ocular Disease Diagnosis: A Review. Computers. 2025;14:298. [DOI:10.3390/computers14080298]
[7] Charng J, Alam K, Swartz G, Deep learning: applications in retinal and optic nerve diseases. Clinical and Experimental Optometry. 2022. [DOI:10.1080/08164622.2022.2111201]
[8] Gao Z, Jin K, Yan Y, End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning. Graefe's Archive for Clinical and Experimental Ophthalmology. 2022;260:1663-1673. [DOI:10.1007/s00417-021-05503-7]
[9] Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, et al. Artificial intelligence and deep learning in ophthalmology. British Journal of Ophthalmology. 2019;103(2):167-175.[DOI:10.1136/bjophthalmol-2018-313173]
[10] Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402-2410. [DOI:10.1001/jama.2016.17216]
[11] Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for diabetic retinopathy. NPJ Digital Medicine. 2018;1:39. [DOI:10.1038/s41746-018-0040-6]
[12] Grassmann F, Mengelkamp J, Brandl C, Harsch S, Zimmermann ME, Linkohr B, et al. A deep learning algorithm for prediction of age-related eye disease. Nature Medicine. 2018;24(9):1347-1350. [DOI:10.1038/s41591-018-0100-9]
[13] Li Z, He Y, Keel S, Meng W, Chang RT, He M. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology. 2018;125(8):1199-1206. [DOI:10.1016/j.ophtha.2018.01.023]
[14] De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine. 2018;24(9):1342-1350. [DOI:10.1038/s41591-018-0107-6]
[15] Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology. 2017;124(7):962-969. [DOI:10.1016/j.ophtha.2017.02.008]
[16] Haq NU, Waheed T, Ishaq K, Computationally efficient deep learning models for diabetic retinopathy detection: a systematic literature review. Artificial Intelligence Review. 2024;57:309. [DOI:10.1007/s10462-024-10942-9]
[17] Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bogunović H. Artificial intelligence in retina. Progress in Retinal and Eye Research. 2018;67:1-29. [DOI:10.1016/j.preteyeres.2018.07.004]
[18] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-444. [DOI:10.1038/nature14539]
[19] Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118.[DOI:10.1038/nature21056]
[20] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016:770-778.[DOI:10.1109/CVPR.2016.90]
- Abstract Viewed: 17 times
- PDF Downloaded: 10 times