Deep Learning Architectures for Early Detection of Diabetic Retinopathy in Retinal Image Analysis
DOI:
https://doi.org/10.69996/jcai.2025026Keywords:
Diabetic Retinopathy, Deep Learning, Retinal Image Analysis, Convolutional Neural Networks, Medical ImagingAbstract
This paper explores the application of deep learning architectures in retinal image analysis, with a particular focus on the early detection of diabetic retinopathy (DR). Leveraging large-scale retinal imaging datasets, the study examines the diagnostic capacity of advanced neural models in identifying
subtle pathological markers such as microaneurysms, hemorrhages, and exudates at the earliest clinical stages. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid deep
learning frameworks are evaluated for their performance in image classification and lesion localization. Results demonstrate that CNN-based architectures achieve an accuracy of 94.2%, with sensitivity and
specificity values exceeding 93%, underscoring their robustness in medical image interpretation. Hybrid
models integrating CNN and attention mechanisms further enhance feature discrimination, achieving 95.1%
accuracy and improved area under the curve (AUC) metrics. RNN-driven architectures exhibit
competitive performance, particularly in sequential image feature learning, with overall accuracy
surpassing 92%. The findings highlight the transformative role of deep learning in automating ophthalmic
diagnostics, enabling scalable, precise, and timely detection of diabetic retinopathy to mitigate
preventable vision loss.
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