Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks

Zenghai Chen, Hong Fu, Wai Lun Lo, Zheru Chi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

45 Citations (Scopus)


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.

Original languageEnglish
Number of pages1
JournalJournal of Healthcare Engineering
Publication statusPublished - 1 Jan 2018

ASJC Scopus subject areas

  • Biotechnology
  • Surgery
  • Biomedical Engineering
  • Health Informatics


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