Survey of Strabismus Detection Techniques
Abstract
Strabismus, or “crossed-eyes,” is one of the most
common ocular diseases. Strabismus has a serious impact on
human life. Patients with strabismus not only have visual but also
psychological and social effects from their condition. In adults, one
study showed that large-angle horizontal strabismus could affect
one’s ability to gain employment. This appeared to be more
important for women’s employability than men. These
psychosocial effects may be influenced by whether the degree of
ocular misalignment is detectable by those with whom they have
contact. If the strabismus is not detectable, presumably the
observers’ negative feelings for strabismus would not be invoked.
As a result, a timely strabismus screening becomes important and
essential for preventing strabismus. So far, there are multiple ways
to complete strabismus screening. Traditional strabismus
screening is conducted manually by ophthalmologists through
many tests, such as the cover and uncover test, prism cover test
and the Hirschberg test. The proposed method uses a frontal facial
image from a patient, and it measures the deviation of the
positional similarity of two eyes within the image, which aims to
provide ophthalmologists with interpretable information for the
diagnosis of strabismus.
Keywords:
Strabismus Detection, Convolutional Neural Network, K-Nearest Neighbors, Support Vector MachinePublished
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