## 5 Conclusions  
This study proposes an online refractive error screening solution centered on CFGN. Through multi-channel information fusion and a CFG module, CFGN significantly enhances the accuracy of refractive error screening. Extensive comparative experiments, transfer learning experiments, and ablation experiments were conducted on our self-built training sets. The experimental results show that fine-tuning the pretrained model with real-human-eye data further improves the model's accuracy in refractive error screening tasks, particularly in small-sample scenarios, where the improvement is especially noticeable. Compared to traditional networks such as ResNet-50 and Vit, CFGN demonstrated outstanding performance in refractive error screening, achieving an accuracy (\(ACC1\)) of 92.7%. In addition, this study validated the feasibility of the transfer learning-based approach and pretrained refractive error screening task using datasets from two different regions. This technology provides a standardized solution for online vision screening using data from different regions. Conceptualization: [PERSON]; methodology: [PERSON] and [PERSON]. ([PERSON]): formal analysis and investigation: [PERSON] and [PERSON]. ([PERSON]); writing--original draft preparation: [PERSON] and [PERSON]. ([PERSON]); writing--review and editing: [PERSON]. ([PERSON]), [PERSON] and [PERSON]; visualization:  
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