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.

翻译：糖尿病视网膜病变是糖尿病的严重并发症，若未及时治疗可能导致永久性失明。早期准确诊断该疾病对成功治疗至关重要。本文提出一种基于单张眼底视网膜图像的深度学习方法，用于糖尿病视网膜病变的检测与分级。该模型采用迁移学习策略，使用两个当前最优的预训练模型作为特征提取器，并在新数据集上进行微调。模型使用包含APTOS 2019数据集在内的大规模多中心公开数据集进行训练，在糖尿病视网膜病变检测与分级任务中取得了显著性能，超越了现有文献报道结果。在二分类任务中，该方法准确率达98.50%，灵敏度99.46%，特异度97.51%。在分级任务中，二次加权卡帕系数达93.00%，准确率89.60%，灵敏度89.60%，特异度97.72%。该方法可作为可靠的糖尿病视网膜病变筛查与分级工具，具有提升临床决策与患者护理水平的巨大潜力。