Diabetes is a persistent disease influencing the internal organs when the pancreas losses its functionality to produce insulin in appropriate amount. Ion latter stages, it severely influences the circulatory system and vision system by damaging retina. Such an ailment is referred as Diabetic Retinopathy (DR) and is a condition of an ailment where the retina is harmed on the grounds that fluid breaks away from the walls of blood vessels into the retina. The diagnosing features for DR comprises of features occurring in and around the regions of blood vessel zone which will result into exudes, hemorrhages, microaneurysms and generation of textures on the albumen region of eye balls. In this study we present a new method relying on cascaded neural network for high precision detection of DR and comparison of its classifications proficiency is drawn out with various DR systems. The majority of the studyed systems are profoundly advanced regarding the analyzed fundus images is catching up to the human ophthalmologist’s characterization capacities. © 2017, Indian Journal of Public Health Research and Development. All rights reserved.