Diabetic Retinopathy (DR) is an ocular condition that can manifest in individuals living with Diabetes Mellitus (DM). Retinal fundus examinations must be conducted on DM individuals as early identification and treatment of DR can reduce the risk of impaired vision or blindness. The manual diagnosis of DR conducted by eye-care professionals can be tedious and time-consuming, especially during mass screenings. Deep learning (DL) techniques are being used to provide automated diagnosis of DR. This study adopted two CNN (VGG 19 and ResNet50) models for the binary classification of DR (Non-referable DR and Referable DR). Both models were trained and validated with retinal fundus images from publicly available datasets. After training with Kaggle dataset, VGG 19 and ResNet50 models achieved accuracies of 94.3% and 96.9% respectively. For external validation, varying levels of accuracy, sensitivity and specificity were obtained for the two models on different datasets. The sensitivity of the VGG 19 model for the Messidor 2 dataset was 78.8% while the sensitivity of the ResNet50 model for the Indian Diabetic Retinopathy Image Dataset (IDRiD) was 85.7%. Findings in this study have shown that DR detection with deep learning techniques can serve as an assistive tool for eye-care professionals in the future.