| Sumario: | Diabetic retinopathy (DR) has become a major cause of preventable blindness, and its early detection is crucial for maintaining vision in diabetic patients. Existing computer‐aided diagnosis (CAD) systems still face challenges in detecting early‐stage lesions, integrating local and global features, and maintaining consistent performance in binary and multiclass settings. To address these limitations, this study proposes a relative‐efficient fusion attention framework (REFA‐DINO) that integrates a convolutional local‐feature learning module with a transformer‐based global contextual method. The architecture employs a relative‐efficient fusion adapter (REFA) block comprising two branches, EfficientNet and attention‐enhanced patch embedding. EfficientNet extracts lesion‐sensitive retinal features, and an attention‐enhanced patch‐embedding module is used to generate token representations. An adaptive relative position‐aware cross‐attention mechanism is introduced to enable effective interaction among heterogeneous features, which are further processed through the DINO‐based transformer module. The extracted global representations are passed to the linear classification head for DR classification. REFA‐DINO is evaluated on two diverse and large‐scale fundus imaging datasets, EyePACS and APTOS, for binary and multiclass classification. The proposed framework achieved accuracies of 98.60% and 93.77% for binary classification and 85.52% and 87.51% for multiclass classification on the APTOS and EyePACS datasets, respectively. A comparative analysis against state‐of‐the‐art methods, cross‐corpora evaluation, and an ablation study are also conducted to demonstrate the effectiveness of the proposed REFA‐DINO. Compared with state‐of‐the‐art methods, the REFA‐DINO framework enhances binary classification on both the EyePACS and APTOS datasets, with significant improvements over the strongest baselines. The proposed approach attains up to 6.19% and 9.39% improvement in the binary and multiclass classification accuracy on EyePACS. Likewise, it achieves an improvement of 2.64% on the binary classification score on APTOS with competitive multiclass classification performance. Experimental findings highlight the robustness and generalizability of the REFA‐DINO framework in classifying DR.
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