A Hybrid Swin Transformer and <scp>EfficientNetB2</scp>‐Based Framework for Automated <scp>ROP</scp> Detection in Premature Infants

ABSTRACT Retinopathy of prematurity (ROP) is the leading cause of childhood blindness, and early and accurate detection is crucial for timely intervention and vision protection. To address the challenges of subtle lesion features and class imbalance in ROP images, this study proposes a hybrid deep learning model that integrates a generative‐discriminative collaborative mechanism and a multi‐module feature fusion strategy, which combines the local detail extraction of EfficientNet‐B2 with the global context modeling of swin transformer to enhance the robustness of fine‐grained lesion perception and classification. Experimental evaluation on the dataset shows that our model achieves 96.71% accuracy and 97.65% specificity, significantly outperforming mainstream baseline models. The performance improvement has been statistically validated ( p &lt; 0.05). These results highlight the effectiveness of the model in addressing the challenges of ROP classification, providing a promising solution for intelligent assisted diagnosis, facilitating early disease warning, and promoting the application of artificial intelligence in ophthalmology.