Published Paper Details:

Diabetic retinopathy is the diminishment of the eyeball lifeblood vessels due to difficulty of diabetes, which can later lead to dropping of eyesight. The problem have only one solution through the use of a eyeball covering system that would identifiy the eyeball harm at an primal phase. This paper suggest the use of analysis working and breakdown techniques for the disclosure of lifeblood vessels, exudation and microaneurysms (are the most typical lesions of diabetic retinopathy, but also present in other pathologies that affect microvessels). The eyeball fundus image (as the process whereby reflected light is used to obtain a two-dimensional (2D) representation of the 3D, semitransparent, retinal tissues projected on to the imaging plane) is divided into four substitute images. Several features are educe from the eyeball fundus image. Haar wavelet transformations are applied for feature extraction this prosses comes after feature selection. Principal component analysis technique is then applied for greter feature selection. Back propagation neural network(Back-propagation is just a way of propagating the total loss back into the neural network to know how much of the loss every node is responsible for, and subsequently updating the weights in such a way that minimizes the loss by giving the nodes with higher error rates lower weights and vice versa.) and one rule classifier techniques are used for the classifying the images as diabetic or non-diabetic.