  The retina is an essential component of the visual system, and maintaining
eyesight depends on the timely and accurate detection of disorders. The
early-stage detection and severity classification of Diabetic Retinopathy (DR),
a significant risk to the public's health is the primary goal of this work.
This study compares the outcomes of various deep learning models, including
InceptionNetV3, DenseNet121, and other CNN-based models, utilizing a variety of
image filters, including Gaussian, grayscale, and Gabor. These models could
detect subtle pathological alterations and use that information to estimate the
risk of retinal illnesses. The objective is to improve the diagnostic processes
for DR, the primary cause of diabetes-related blindness, by utilizing deep
learning models. A comparative analysis between Greyscale, Gaussian and Gabor
filters has been provided after applying these filters on the retinal images.
The Gaussian filter has been identified as the most promising filter by
resulting in 96% accuracy using InceptionNetV3.
