Deep Learning for Diabetic Retinopathy Detection from Retinal Images

Diabetic retinopathy (DR) is a progressive microvascular complication of diabetes and is today consid- ered a major

cause of avoidable blindness on a global scale. This should be detected at a young age, but the conventional approach to

screening uses manual interpretation of the retina fundus images, which is laborious, subjective, and hard to automatically do

on a large population. The proposed project presents an automated deep learning model based on the use of sophisticated convolutional neural networks (CNNs) to identify stage of DR precisely using the retinal images. It starts with the creation of a

heterogeneous dataset and proceeds to a powerful preprocessing step comprised of normalization, contrast enhancement, image

re-scaling, and illumination removal to enhance the appearance of lesions. More data augmentation methods including rotation,

flipping, zooming, noise infusion also improve the generalization of the models and deal with the issue of class imbalance. A

discriminative retinal feature extractor with a small CNN architecture, built on the latest models, like EfficientNet or ResNet, is

trained to recognize images according to various levels of severity of DR. The diagnostic reliability is evaluated with rigor by

accuracy, precision, recall, F1-score, sensitivity, specificity, and ROCAUC on model performance to be sure that the model

performance is reliable. The resulting trained model is embedded into a user-friendly web-based application which enables

clinicians to provide retinal images and get quick and evidence-based predictions with heatmap visualizations to read between

the lines. This system provides better screening of DR before symptoms, encourages diagnosing ophthalmology at scale, and

shows a promising influence of deep learning on medical image processing