@inproceedings{2d4d36438c954848b9d86964e42c2634,
title = "Accessing refractive errors via eccentric infrared photorefraction based on deep learning",
abstract = "Eccentric infrared photorefraction is an attractive vision screening method which is widely used for uncooperative subjects, such as infants and toddlers. Unlike conventional slope-based photorefraction, a deep neural network is used to predict refractive error in this study. Total 1216 ocular image were collected by a homemade photorefraction device, whose corresponding refractive error was measured by a commercial autorefractor device, to create a series of dataset for our deep neural network. The mean squared error of the preliminary result is ±0.9 diopter, which indicates its feasibility and can be improved with bigger database.",
keywords = "Deep learning, Digital imaging processing, Optical system, Photorefraction, Refractive error",
author = "Yang, {Chia Chi} and Su, {Jian Jia} and Li, {Jie En} and Zhu, {Zhi Yu} and Tseng, {Jin Shing} and Cheng, {Chu Ming} and Tien, {Chung Hao}",
year = "2019",
month = jan,
day = "1",
doi = "10.1117/12.2542652",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Masafumi Kimata and Valenta, {Christopher R.}",
booktitle = "SPIE Future Sensing Technologies",
address = "美國",
note = "SPIE Future Sensing Technologies 2019 ; Conference date: 14-11-2019",
}