  With the prevalence of Diabetes, the Diabetes Mellitus Retinopathy (DR) is
becoming a major health problem across the world. The long-term medical
complications arising due to DR have a significant impact on the patient as
well as the society, as the disease mostly affects individuals in their most
productive years. Early detection and treatment can help reduce the extent of
damage to the patients. The rise of Convolutional Neural Networks for
predictive analysis in the medical field paves the way for a robust solution to
DR detection. This paper studies the performance of several highly efficient
and scalable CNN architectures for Diabetic Retinopathy Classification with the
help of Transfer Learning. The research focuses on VGG16, Resnet50 V2 and
EfficientNet B0 models. The classification performance is analyzed using
several performance metrics including True Positive Rate, False Positive Rate,
Accuracy, etc. Also, several performance graphs are plotted for visualizing the
architecture performance including Confusion Matrix, ROC Curve, etc. The
results indicate that Transfer Learning with ImageNet weights using VGG 16
model demonstrates the best classification performance with the best Accuracy
of 95%. It is closely followed by ResNet50 V2 architecture with the best
Accuracy of 93%. This paper shows that predictive analysis of DR from retinal
images is achieved with Transfer Learning on Convolutional Neural Networks.
