Comparative Feature-Guided Regression Network with a Model-Eye Pretrained Model for Online Refractive Error Screening

Jiayi Wang, Tianyou Zheng (), Yang Zhang, Tianli Zheng and Weiwei Fu ()
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Jiayi Wang: School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
Tianyou Zheng: Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou 215163, China
Yang Zhang: Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou 215163, China
Tianli Zheng: Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou 215163, China
Weiwei Fu: School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China

Future Internet, 2025, vol. 17, issue 4, 1-26

Abstract: With the development of the internet, the incidence of myopia is showing a trend towards younger ages, making routine vision screening increasingly essential. This paper designs an online refractive error screening solution centered on the CFGN (Comparative Feature-Guided Network), a refractive error screening network based on the eccentric photorefraction method. Additionally, a training strategy incorporating an objective model-eye pretraining model is introduced to enhance screening accuracy. Specifically, we obtain six-channel infrared eccentric photorefraction pupil images to enrich image information and design a comparative feature-guided module and a multi-channel information fusion module based on the characteristics of each channel image to enhance network performance. Experimental results show that CFGN achieves an accuracy exceeding 92% within a ±1.00 D refractive error range across datasets from two regions, with mean absolute errors (MAEs) of 0.168 D and 0.108 D, outperforming traditional models and meeting vision screening requirements. The pretrained model helps achieve better performance with small samples. The vision screening scheme proposed in this study is more efficient and accurate than existing networks, and the cost-effectiveness of the pretrained model with transfer learning provides a technical foundation for subsequent rapid online screening and routine tracking via networking.

Keywords: refractive error screening; pretrained model; deep learning; transfer learning; objective model eye (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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