An improved strabismus screening method with combination of meta-learning and image processing under data scarcity.

ABSTRACT:

Purpose

Considering the scarcity of normal and strabismic images, this study proposed a method that combines a meta-learning approach with image processing methods to improve the classification accuracy when meta-learning alone is used for screening strabismus.

Methods

The meta-learning approach was first pre-trained on a public dataset to obtain a well-generalized embedding network to extract distinctive features of images. On the other hand, the image processing methods were used to extract the position features of eye regions (e.g., iris position, corneal light reflex) as supplementary features to the distinctive features. Afterward, principal component analysis was applied to reduce the dimensionality of distinctive features for integration with low-dimensional supplemen

SUBMITTER: Huang X 

PROVIDER: S-EPMC9355186 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature