This document discusses diabetic retinopathy detection through deep learning techniques. It summarizes previous research that achieved accuracies between 73-92% detecting diabetic retinopathy using convolutional neural networks like VGG16, MobileNetV1, and MobileNetV2. The authors propose using a MobileNet architecture with dense blocks for image classification of diabetic retinopathy. They achieve 96% accuracy, with precision, recall, and F1 scores of 0.95, 0.98, and 0.97 respectively. Early detection of diabetic retinopathy through this approach could help experts treat patients earlier and prevent vision loss.