Abstract Diabetic retinopathy (DR) is a leading cause of preventable blindness, motivating the development of reliable automated screening systems. This work proposes a Robust Deep Ensemble for Diabetic Retinopathy detection (RDE-DR) by analyzing ensemble fusion strategies. Four pre-trained convolutional neural networks (ResNet50, VGG16, VGG19, and DenseNet121) are trained using CLAHE-enhanced APTOS 2019 fundus images and integrated through seven heterogeneous fusion mechanisms, including voting-based, rank-based, and fuzzy-integral-inspired strategies. A consistent evaluation protocol is adopted, incorporating threshold optimization and probabilistic calibration analysis to validate robustness, decision margins, and accuracy–precision trade-offs. Experimental results show that multiple fusion techniques achieve comparable high performance and stable behavior on the APTOS 2019 benchmark, with the best configuration reaching 98.64% accuracy, 98.40% precision, 98.92% recall, 98.66% F1-score, and 99.78% Area-Under-Curve (AUC). Beyond peak accuracy, the study provides insights into ensemble reliability, calibration characteristics, and practical design choices for medical image classification systems. These results show that integrating transfer learning with CLAHE preprocessing and ensemble fusion yields stable experimental performance on the APTOS 2019 benchmark, suggesting potential for future medical decision support. Similar content being viewed by others Introduction Diabetic retinopathy (DR) is a leading cause of vision impairment and preventable blindness worldwide, particularly among individuals with diabetes1. Early and accurate screening is essential to prevent irreversible vision loss and improve patient outcomes2. However, early-stage DR detection remains challenging due to the subtle appearance of pathological features and the reliance on manual interpretation by clinical experts, which is time-consuming and subject to inter-observer variability3. In recent years, advances in artificial intelligence (AI) and deep learning have significantly enhanced automated disease screening, enabling reliable analysis of retinal fundus images4. Convolutional neural networks (CNNs), in particular, have demonstrated strong performance in medical image classification and segmentation tasks. Nevertheless, the scarcity of large, well-annotated medical datasets limits the training of high-capacity