Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks
Joint Authors
Fu, Hong
Chen, Zenghai
Lo, Wai-Lun
Chi, Zheru
Source
Journal of Healthcare Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-04-26
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Strabismus is one of the most common vision diseases that would cause amblyopia and even permanent vision loss.
Timely diagnosis is crucial for well treating strabismus.
In contrast to manual diagnosis, automatic recognition can significantly reduce labor cost and increase diagnosis efficiency.
In this paper, we propose to recognize strabismus using eye-tracking data and convolutional neural networks.
In particular, an eye tracker is first exploited to record a subject’s eye movements.
A gaze deviation (GaDe) image is then proposed to characterize the subject’s eye-tracking data according to the accuracies of gaze points.
The GaDe image is fed to a convolutional neural network (CNN) that has been trained on a large image database called ImageNet.
The outputs of the full connection layers of the CNN are used as the GaDe image’s features for strabismus recognition.
A dataset containing eye-tracking data of both strabismic subjects and normal subjects is established for experiments.
Experimental results demonstrate that the natural image features can be well transferred to represent eye-tracking data, and strabismus can be effectively recognized by our proposed method.
American Psychological Association (APA)
Chen, Zenghai& Fu, Hong& Lo, Wai-Lun& Chi, Zheru. 2018. Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks. Journal of Healthcare Engineering،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1187661
Modern Language Association (MLA)
Chen, Zenghai…[et al.]. Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks. Journal of Healthcare Engineering No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1187661
American Medical Association (AMA)
Chen, Zenghai& Fu, Hong& Lo, Wai-Lun& Chi, Zheru. Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks. Journal of Healthcare Engineering. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1187661
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187661