  Patients with long-standing diabetes often fall prey to Diabetic Retinopathy
(DR) resulting in changes in the retina of the human eye, which may lead to
loss of vision in extreme cases. The aim of this study is two-fold: (a) create
deep learning models that were trained to grade degraded retinal fundus images
and (b) to create a browser-based application that will aid in diagnostic
procedures by highlighting the key features of the fundus image. In this
research work, we have emulated the images plagued by distortions by degrading
the images based on multiple different combinations of Light Transmission
Disturbance, Image Blurring and insertion of Retinal Artifacts. InceptionV3,
ResNet-50 and InceptionResNetV2 were trained and used to classify retinal
fundus images based on their severity level and then further used in the
creation of a browser-based application, which implements the Integration
Gradient (IG) Attribution Mask on the input image and demonstrates the
predictions made by the model and the probability associated with each class.
