## 3 Experiment and Results
### Comparative Experiment  
In this section, we compare the proposed CFGN model with several commonly used deep learning models, including VGG-16 [44], Densenet-121 [45], AlexNet [46], ResNet-18 [8], ResNet-50 [8], ResNeXt-50 [47], Se-ResNet-50 [48], ViT [31], Swin Transformer [49], REDNet [16], and ResNet-50-6. Notably, ResNet-50-6 is a six-channel input model based on ResNet-50 designed to verify the performance improvement of multi-channel images in refractive error prediction tasks. It is important to note that conventional deep learning regression models are trained using a single eccentric infrared pupil image, whereas CFGN and ResNet50-6 are trained using six-channel eccentric infrared pupil images.  
As shown in Tables 3 and 4, the experimental results show that on Dataset1 and Dataset3 (full-range dataset), models such as VGG-16, Densenet-121, AlexNet, ResNet-18, ResNet-50, ResNeXt-50, Se-ResNet-50, Vit, REDNet [16], and Swin Transformer have similar performance, but their \(ACC1\) accuracy does not exceed 90%. This indicates that existing network architectures still have room for optimization in refractive error screening tasks. In contrast, Both CFGN and ResNet50-6 achieved higher \(ACC1\) and \(ACC0.5\) on both datasets compared to conventional models, fully validating the effectiveness of multi-channel image inputs. Notably, CFGN achieved the best performance across all evaluation metrics, demonstrating its exceptional capabilities.  
On Dataset2 and Dataset4 (0.5 D precision datasets), the performance of models such as VGG-16, Densenet-121, AlexNet, ResNet-18, ResNet-50, ResNeXt-50, REDNet, and Se-ResNet-50 was similar. However, the Vit, based on the Transformer architecture, failed to fully converge due to the limited sample size, resulting in suboptimal performance. In the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Parameter Value** \\ \hline System environment & Linux \\ CPU & Intel(R) Xeon(R) Platinum 8336c \\ GPU (Graphics Processing Unit) & NVIDIAGeForce RTX4090 \\ Development language & Python3.10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Development environment parameters.  
comparative experiment data, only CFGN achieved an ACC1 accuracy of over 80% on the datasets from both regions, maintaining a leading advantage across all metrics. This further confirms that CFGN can effectively extract feature information from eccentric infrared pupil images even under small-sample conditions, enabling it to better perform refractive error screening tasks.  
In summary, the CFGN we designed demonstrates outstanding performance in refractive error screening tasks, significantly outperforming mainstream models such as VGG-16, Densenet-121, AlexNet, ResNet-18, ResNet-50, ResNeXt-50, Se-ResNet-50, Vit, REDNet, and Swin Transformer. Specifically, on the small-sample datasets, Dataset2 and Dataset4, CFGN achieved a 5% to 9% improvement in _ACC_1 accuracy compared to conventional models, while on the full-range datasets, Dataset1 and Dataset3, it achieved a 3% to 5% performance boost in _ACC_1 accuracy. This result fully validates the robustness of CFGN across different data scales, highlighting its practical value in refractive error screening tasks.