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

Public Health
Medicine

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