Strabismus recognition using eye-tracking data and convolutional neural networks

Zenghai CHEN, Hong FU, Wai-Lun LO, Zheru CHI

Research output: Contribution to journalArticlespeer-review

19 Citations (Scopus)

Abstract

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. Copyright © 2018 Zenghai Chen et al.
Original languageEnglish
Article number7692198
JournalJournal of Healthcare Engineering
Volume2018
Early online date26 Apr 2018
DOIs
Publication statusPublished - 2018

Citation

Chen, Z., Fu, H., Lo, W.-L., & Chi, Z. (2018). Strabismus recognition using eye-tracking data and convolutional neural networks. Journal of Healthcare Engineering, 2018. Retrieved from https://doi.org/10.1155/2018/7692198

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