  Diabetic retinopathy (DR) is the primary cause of vision loss among grownup
people around the world. In four out of five cases having diabetes for a
prolonged period leads to DR. If detected early, more than 90 percent of the
new DR occurrences can be prevented from turning into blindness through proper
treatment. Despite having multiple treatment procedures available that are well
capable to deal with DR, the negligence and failure of early detection cost
most of the DR patients their precious eyesight. The recent developments in the
field of Digital Image Processing (DIP) and Machine Learning (ML) have paved
the way to use machines in this regard. The contemporary technologies allow us
to develop devices capable of automatically detecting the condition of a
persons eyes based on their retinal images. However, in practice, several
factors hinder the quality of the captured images and impede the detection
outcome. In this study, a novel early blind detection method has been proposed
based on the color information extracted from retinal images using an ensemble
learning algorithm. The method has been tested on a set of retinal images
collected from people living in the rural areas of South Asia, which resulted
in a 91 percent classification accuracy.
