A Hybrid-CNN Framework for Accurate Early Detection of Diabetic Retinopathy

If left untreated, diabetic retinopathy (DR) can cause significant vision
damage, so early and accurate diagnosis is essential. This study presents a new
approach to improve the accuracy of DR analysis using a hybrid convolutional neural
network (CNN) model. Leveraging the capabilities of the ResNet50 and InceptionV3
algorithms, the model attempts to extract complex features from fundus images—
features that are important for early detection of drug resistance. The problem is
detecting DR early when symptoms are mild, making automated methods inaccurate.
Adding other models such as DenseNet and Xception will increase the accuracy to
97.2%. Additionally, an extension uses the Flask framework to create an easy-to-use
frontend with validation, which simplifies user testing. This comprehensive approach
not only promises a better classification of DR, but also highlights the importance of
early diagnosis; thereby limiting the risks of vision loss associated with this disabling
disease is reduced.