CBR-Enhanced ResNet50 for Five-Class Diabetic Retinopathy Grading: An Ablation-Based Study

Abstract

Diabetic retinopathy (DR) is a common complication of diabetes and one of the leading causes of preventable vision loss worldwide. Because the manual grading of color fundus images is slow and depends on the availability of trained specialists, automated screening tools are needed. This study proposes a lightweight channel-wise refinement strategy for automatic five-class DR grading, built on a ResNet50 backbone. Two custom blocks are evaluated: CBR, which applies a 3 × 3 convolution, batch normalization, and a ReLU activation to make the channel representation more compact, and CBS, which applies a 3 × 3 convolution, batch normalization, and a SiLU activation to reinforce local spatial features. On the Diabetic Retinopathy Balanced dataset, the baseline ResNet50 reached an accuracy of 90.77%, a precision of 90.60%, a recall of 90.79%, and an F1-score of 90.64%. In the ablation study, the best configuration was ResNet50 + CBR, with an accuracy of 91.85%, a precision of 91.75%, a recall of 91.88%, and an F1-score of 91.76%. The full CBR-CBS Hybrid ResNet50 was close behind, with an accuracy of 91.81% and an F1-score of 91.71%. The CBR block accounts for most of this improvement, which suggests that channel-wise refinement helps the model separate subtle lesion patterns. These results establish lightweight channel-wise refinement (CBR) as an effective, compact, and interpretable enhancement of ResNet50 for automated five-class DR grading, delivering a consistent multi-metric gain over the baseline and accuracy competitive with the literature, which makes it a promising solution for large-scale screening.