Diabetic retinopathy (DR) is a disease resulting from diabetes complications, causing
non-reversible damage to retina blood vessels. DR is a leading cause of blindness if not detected
early. The currently available DR treatments are limited to stopping or delaying the deterioration of
sight, highlighting the importance of regular scanning using high-efficiency computer-based systems
to diagnose cases early. The current work presented fully automatic diagnosis systems that exceed
manual techniques to avoid misdiagnosis, reducing time, effort and cost. The proposed system
classifies DR images into five stages—no-DR, mild, moderate, severe and proliferative DR—as well
as localizing the affected lesions on retain surface. The system comprises two deep learning-based
models. The first model (CNN512) used the whole image as an input to the CNN model to classify it
into one of the five DR stages. It achieved an accuracy of 88.6% and 84.1% on the DDR and the APTOS
Kaggle 2019 public datasets, respectively, compared to the state-of-the-art results. Simultaneously,
the second model used an adopted YOLOv3 model to detect and localize the DR lesions, achieving
a 0.216 mAP in lesion localization on the DDR dataset, which improves the current state-of-the-art
results. Finally, both of the proposed structures, CNN512 and YOLOv3, were fused to classify DR
images and localize DR lesions, obtaining an accuracy of 89% with 89% sensitivity, 97.3 specificity
and that exceeds the current state-of-the-art results.