Fuzzy Choquet Ensemble Deep Learning Approach for Diabetic Retinopathy Detection

Abstract

Diabetic retinopathy (DR) remains a leading cause of vision impairment worldwide, necessitating early detection to prevent irreversible blindness. Manual screening processes are time-consuming and constrained by resource limitations, emphasizing the need for automated and efficient diagnostic systems. Deep learning models have delivered notable results, but further refinement is still possible. In this work, we provide an advanced ensemble deep learning framework for automated DR screening, leveraging the Choquet fuzzy integral to aggregate the outputs of multiple state-of-the-art convolutional neural networks (CNNs), including DenseNet121, Xception, and InceptionResNetV2. These pretrained models are fine-tuned on retinal fundus images to extract complementary features, which are then dynamically combined using the Choquet fuzzy integral, thereby improving predictive accuracy and robustness. Our model was trained and evaluated on a comprehensive real-world dataset, outperforming conventional deep learning and traditional machine learning baselines in terms of accuracy, sensitivity, and specificity. Experiments demonstrated the model’s resilience to dataset variability and its strong generalization capabilities. The proposed approach attained an average recall of 84.09 %, a precision of 83.09 %, an F1-score of 82.96 %, and an accuracy of 84.09 %. These findings substantiate the potential of ensemble learning techniques in advancing the effectiveness of automated DR screening systems.