Automated Grading of Diabetic Retinopathy in Retinal Fundus Images using Deep Learning The importance of early detection of Diabetic Retinopathy (DR) and the time consuming and error prone nature of manual screening of the disease necessitates an automated system that can identify and grade the disease reliably. In this paper, we propose a deep learning solution for automated grading of DR from retinal fundus images. Specifically, we employ Convolutional Neural Network (CNN) architectures which have achieved stateoftheart performance in image recognition tasks. The experimental results demonstrate that the best performing deep learning model achieves a high sensitivity of 94.3% and specificity of 95.5%(for DRnoDR classification) and a quadratic weighted kappa score  (kappa2)  of 0.88 (for grading from 0 through 4), indicating an improved  kappa2  score compared to the stateoftheart method ( kappa2  score of 0.81).

A novel automated system of discriminating Microaneurysms in fundus images Abstract   Diabetic retinopathy, a chronic disease in diabetic patients leads to Vision loss, by disabling microvascular complications, if not recognized and cured at the earlier stage. This article explores a novel and reliable method for automatic early detection of Microaneurysms (MA) in fundus images. Microaneurysms characterized by small red spots on the retina, the red lesions are symptoms of early stage of DR. Development of an automated screening system would assist an ophthalmologist in diagnosing DR at an early stage. Hence, in this paper, a novel feature extraction technique using a Local Neighborhood Differential Coherence Pattern (LNDCP) is proposed. In this method, texture characteristics needed for classification by Feed Forward Neural Network (FFNN) is captured efficiently. The performance of the algorithm is validated using experiments on Retinopathy Online Challenge (ROC) public dataset and a single realtime dataset, AGAR300. Efficiency of the algorithm is benchmarked with stateofart approaches and a Freeresponse Receiver Operating Characteristic (FROC) score of 0.481 and 0.442 have been achieved for ROC and AGAR300 respectively.