Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/78582
Title: | Strabismus recognition using eye-tracking data and convolutional neural networks | Authors: | Chen, ZH Fu, H Lo, WL Chi, ZR |
Issue Date: | 2018 | Source: | Journal of healthcare engineering, 2018, 7692198 | 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. | Publisher: | Hindawi Publishing Corporation | Journal: | Journal of healthcare engineering | ISSN: | 2040-2295 | EISSN: | 2040-2309 | DOI: | 10.1155/2018/7692198 | Rights: | © 2018 Zenghai Chen et al. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The following publication 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, 7692198 is available at https://doi.org/10.1155/2018/7692198 |
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