Diabetic retinopathy is one of the most frightening complications of diabetes mellitus affecting the working-age population worldwide leading to irreversible blindness if left untreated. A major challenge is early detection, which is very important for treatment success. Presently, detecting diabetic retinopathy is a time-, effort-, and cost-consuming manual process where ophthalmologists identify diabetic retinopathy by the presence of lesions associated with the vascular abnormalities caused by the disease. However, the expertise and equipment needed are often lacking in places where the rate of diabetes in the populace is high and detection is most wanted. As the number of persons diagnosed with diabetes continues to increase, the infrastructure required to prevent diabetes-induced blindness will become even more important. The need for an automated approach to diabetic retinopathy screening has long been recognized, and several recent efforts have made good progress using image classification, pattern recognition, and machine learning. With color fundus photography as input, we proposes a novel approach to detection of diabetic retinopathy based on deep learning techniques ideally resulting in a model with realistic clinical potential. A hybrid configuration of a 2-dimensional Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) is proposed to leverage feature representation of CNN and fast classification learning of ELM. A publicly available benchmark dataset consisting of 35,126 retina scan images that are resized into 224x224 pixels are used to train, test and evaluate the proposed model. To measure the performance of the proposed model, the accuracy, precision, sensitivity, F1-score, and Cohen’s kappa score were determined. Model results reveal that the CNN-ELM approach achieved an accuracy of 94.72%, 92.71% precision, 0.7834 sensitivity, an F1-score of 0.8492 and an average Kappa-score of 0.6792 for the multi-class classification. These results demonstrate the proposed hybrid model’s ability to detect and classify diabetes retinopathy.