Early Diabetic Retinopathy Detection Using Deep Learning Algorithms

Abstract

Abstract: Diabetic Retinopathy (DR) is caused by the high blood glucose level which causes micro vascular complications in eyes which lead to vision loss. (MA)Microaneurysms formation in the retinal is sign of diabetic rent ropy which can be cured at early stage. Finding Microaneurysms (MA) presence in the eye image and recognition of diabetic retinopathy at early stage is difficult. Technology of deep learning makes it easier and efficient for analysis of eyes to detect MA presence which can be done by image detection and segmentation with good performance and accuracy using deep learning algorithms. This will help us to differentiated between affected retina and non-affected one. The given system can use deep convolution neural network for semantic segmentation of fundus images which increase efficiency and accuracy of NPDR (non- proliferated diabetic retinopathy) prediction. Keywords: PCA Principle Component Analysis DA Diabetic Retinopathy MA Microaneurysms NPDR Non proliferated diabetic retinopathy BPNN Back propagation neural network