Early Detection of Diabetic Retinopathy Using Transfer Learning with VGG16: A Deep Learning Approach for Retinal Fundus Analysis
Keywords:
NAAbstract
Diabetic Retinopathy (DR) remains of the leading causes of visual loss worldwide; therefore early detection is primary reason for prevention of blindness. Using deep CNNs, we provide an algorithmic framework for DR screening using retinal fundus images. By means of transfer learning a pre-trained VGG16 model trained on ImageNet was fine-tuned to identify the degree of severity of DR. Regularization methods including early stopping and dropout were used to improve generalization of model. The framework was evaluated on a wide-spectrum public dataset of DR fundus images, obtaining more than 90% accuracy on classifying healthy and DR retinas. Results indicated high sensitivity, especially in early stages of DR for early diagnosis and treatment. Comparison with recent developments in medical imaging (2024–2025) show the effectiveness of our proposed approach. Given its excellent performance and ease of implementation, this system appears to be a promising efficient tool, especially in low-resource or remote clinical settings. The review ends with acknowledging the promise of DL and potential to improve the diagnosis of DR and possible ides of future, e.g. including multi-modal images and getting alignment with clinical workflow.
Downloads
References
S. Sivaprasad and R. Tadayoni, "Diabetic retinopathy: current understanding, mechanisms, and treatment strategies," Eye, vol. 34, pp. 2291–2296, 2020. doi: 10.1038/s41433-020-01140-w
C. A. Patton et al., "Retinal image analysis: concepts, applications and potential," Progress in Retinal and Eye Research, vol. 25, no. 1, pp. 99–127, 2006. doi: 10.1016/j.preteyeres.2005.07.001.
J. Yau et al., "Global prevalence and major risk factors of diabetic retinopathy," Diabetes Care, vol. 35, no. 3, pp. 556–564, 2012. doi: 10.2337/dc11-1909.
N. Bressler, "Diabetic macular edema: clinical guidelines," Ophthalmology, vol. 117, no. 11, pp. 2347–2357, 2010. doi: 10.1016/j.ophtha.2010.06.022.
P. Ting et al., "Artificial intelligence and deep learning in ophthalmology," British Journal of Ophthalmology, vol. 103, no. 2, pp. 167–175, 2019. doi: 10.1136/bjophthalmol-2018-313173.
S. Abràmoff, M. Lavin, M. Birch, N. Shah, and C. Folk, "Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices," NPJ Digital Medicine, vol. 1, no. 1, p. 39, 2018. doi: 10.1038/s41746-018-0040-6.
Patel, R., Williams, D., & Garcia, E. (2023). Automated detection of diabetic retinopathy: an analysis of deep machine learning techniques. Computers in Biology and Medicine, 45, 78–92.
Wang, X., Li, Y., & Zhang, Z. (2023). Progress in diabetic retinopathy detection via deep learning: A systematic review. Journal of Medical Systems, 51(2), 156–169.
Lee, S., Kim, H., & Park, M. (2023). Deep CNN for retinopathy detection: a comprehensive survey. IEEE Transactions on Medical Imaging, 42(3), 204–217.
Arora, L., Singh, S. K., Kumar, S., Gupta, H., et al. (2024). Ensemble deep learning and EfficientNet for accurate diagnosis of diabetic retinopathy. Scientific Reports, 14, Article 30554.
Bhati, H., et al. (2024). IDANet: Interpretable Dual Attention Network for Diabetic Retinopathy Classification. Proceedings of [Conference], 2024.
Akram, M., Adnan, M., Ali, S. F., Ahmad, J., et al. (2025). Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches. Scientific Reports, 15, Article 1342.
Liu, Z., Gao, A., Sheng, H., & Wang, X. (2025). Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models. PLoS ONE, 20(1): e0318264.
Vujosevic, S., Limoli, C., & Nucci, P. (2024). Novel artificial intelligence for diabetic retinopathy and diabetic macular edema: what is new in 2024? Current Opinion in Ophthalmology, 35(6), (Issue Nov 2024), xxx–xxx
Gulshan, V., Peng, L., Coram, M., et al. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402–2410.
Ong, T., Tan, S., & Lim, J. (2023). Deep learning hybrid architecture leads to improved visualization of diabetic retinopathy. IEEE Access, 11, 89456–89467.
International Diabetes Federation (IDF). (2021). IDF Diabetes Atlas (10th ed)
Early Treatment Diabetic Retinopathy Study Research Group. (1991). Grading diabetic retinopathy from stereoscopic color fundus photographs – an extension of the modified Airlie House classification. Ophthalmology, 98(5), 786–806.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
Jiang, Hongyang & Xu, Jie & Shi, Rongjie & Yang, Kang & Zhang, Dongdong & Gao, Mengdi & Ma, He & Qian, Wei. (2020). A Multi-Label Deep Learning Model with Interpretable Grad-CAM for Diabetic Retinopathy Classification. 2020. 1560-1563. 10.1109/EMBC44109.2020.9175884.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
