Paper Title
Detection of Intraretinal Microvascular Abnormalities using Deep Neural Networks (DNN)

Diabetes or more precisely Diabetes Mellitus (DM) is a metabolic disorder which happens because of high blood sugar levels in the body. Over the time, diabetes creates eye deficiency also called as Diabetic Retinopathy (DR) which causes major loss of vision. The symptoms that can originate in the retinal area are augmented blood vessels, fluid drip, exudates, hemorrhage, and micro aneurysms. In modern medical science, images are the essential tool for exact diagnosis of patients. In the intervening time estimate of current medical imageries remains multifaceted. In recent times computer vision with Deep Neural Networks can train a model perfectly and level of correctness also will be higher than other neural network models. In this study fundus images containing diabetic retinopathy has been taken into consideration. Model has been trained with three types, back propagation (NN), Deep Neural Network (DNN) and Convolution Neural Network (CNN) after testing models with CPU skilled Neural set of connections gives lowly accurateness because of one unseen layers where as the deep learning models are outperforming NN. The Deep Learning models are capable of quantifying the features as blood vessels, fluid drip, exudates, hemorrhages and micro aneurysms into different classes. Model will calculate the weights which gives severity level of the patient’s eye. The foremost challenge of this study is the accurate verdict of each feature class thresholds. To identify the target class thresholds weighted Fuzzy C-means algorithm has been used. The model will be helpful to identify the proper class of severity of diabetic retinopathy images. Keywords - DM, DR, DNN, CNN