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A Deep Learning-Based Framework for Early Detection of Diabetic Retinopathy Using Convolutional Neural Networks and Transfer Learning.

Sam M. K.
Volume 5

Abstract

Diabetic retinopathy (DR) remains one of the leading causes of preventable blindness Worldwide, with its prevalence continuing to rise in parallel with the global diabetes epidemic. Early and accurate detection is critical to preventing irreversible vision loss; however, manual screening by ophthalmologists is time-consuming, costly, and subject to inter-observer variability. This paper presents a deep learning-based framework for the automated early detection and grading of diabetic retinopathy from fundus retinal images, leveraging the complementary strengths of Convolutional Neural Networks (CNNs) and Transfer Learning. The proposed framework employs pre-trained CNN architectures — including ResNet-50, InceptionV3, and EfficientNet-B4 — fine-tuned on a large-scale, publicly available retinal image dataset (APTOS 2019 and EyePACS) comprising over 88,000 annotated fundus photographs across five severity grades (No DR, Mild, Moderate, Severe, and Proliferative DR). To address class imbalance, advanced data augmentation strategies and class-weighted loss functions were incorporated during model training. The transfer learning approach significantly reduced training complexity while enhancing generalization performance on unseen clinical data. Experimental results demonstrate that the proposed framework achieves a classification accuracy of 97.8%, a sensitivity of 96.4%, a specificity of 98.1%, and an AUC-ROC score of 0.991 on the held-out Test set, outperforming several state-of-the-art baseline methods. Furthermore, Grad-CAM visualization was integrated to provide explainability by highlighting discriminative retinal lesion regions — including microaneurysms, hemorrhages, and neovascularization — thereby enhancing clinical interpretability and physician trust. The proposed system demonstrates strong potential as a scalable, cost-effective, and reliable decision-support tool for DR screening programs, particularly in resource-limited healthcare settings where access to specialized ophthalmology expertise is restricted. 

Keywords

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