CBAM-enhanced convolutional networks for diabetic retinopathy classification in the ODIR dataset

Attention modules may improve fundus-image classification by reweighting informative channels and spatial regions. We evaluated a Convolutional Block Attention Module (CBAM)-enhanced convolutional network for binary diabetic retinopathy classification using preprocessed images from the Ocular Disease Recognition dataset and compared it with VGG-11, VGG-13, ResNet-18, ResNet-34, and ResNet-50 under the reported common training conditions. On the fixed 1,000-image test set, the CBAM model produced 465 true positives, 460 true negatives, 35 false negatives, and 40 false positives, corresponding to 92.5% accuracy, 92.1% precision, 93.0% sensitivity, and a 92.5% F1-score; the reported AUC was 0.96. The model also had a reported inference time of 28 ms, a 35.2 MB file size, and 1.8 billion floating-point operations. These results support the feasibility of a compact attention-enhanced classifier in this controlled image-level evaluation. They do not establish full clinical effectiveness because the study used one public dataset and one archived split, lacked external validation and component-wise ablation, and did not retain paired lesion-level explanation outputs.