Title: | Accessing Refractive Errors via Eccentric Infrared Photorefraction Based on Deep Learning |
Authors: | Yang, Chia-Chi Su, Jian-Jia Li, Jie-En Zhu, Zhi-Yu Tseng, Jin-Shing Cheng, Chu-Ming Tien, Chung-Hao 光電工程學系 Department of Photonics |
Keywords: | refractive error;photorefraction;deep learning;digital imaging processing;optical system |
Issue Date: | 1-Jan-2019 |
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. |
URI: | http://dx.doi.org/10.1117/12.2542652 http://hdl.handle.net/11536/154070 |
ISBN: | 978-1-5106-3112-0 |
ISSN: | 0277-786X |
DOI: | 10.1117/12.2542652 |
Journal: | SPIE FUTURE SENSING TECHNOLOGIES |
Volume: | 11197 |
Begin Page: | 0 |
End Page: | 0 |
Appears in Collections: | Conferences Paper |