  Diabetic retinopathy is a severe complication of diabetes that can lead to
permanent blindness if not treated promptly. Early and accurate diagnosis of
the disease is essential for successful treatment. This paper introduces a deep
learning method for the detection and stage grading of diabetic retinopathy,
using a single fundus retinal image. Our model utilizes transfer learning,
employing two state-of-the-art pre-trained models as feature extractors and
fine-tuning them on a new dataset. The proposed model is trained on a large
multi-center dataset, including the APTOS 2019 dataset, obtained from publicly
available sources. It achieves remarkable performance in diabetic retinopathy
detection and stage classification on the APTOS 2019, outperforming the
established literature. For binary classification, the proposed approach
achieves an accuracy of 98.50, a sensitivity of 99.46, and a specificity of
97.51. In stage grading, it achieves a quadratic weighted kappa of 93.00, an
accuracy of 89.60, a sensitivity of 89.60, and a specificity of 97.72. The
proposed approach serves as a reliable screening and stage grading tool for
diabetic retinopathy, offering significant potential to enhance clinical
decision-making and patient care.
