Exploiting Active Learning in Novel Refractive Error Detection with Smartphones

Abstract

Refractive errors, such as myopia and astigmatism, can lead to severe visual impairment if not detected and corrected in time. Traditional methods of refractive error diagnosis rely on well-trained optometrists operating expensive and importable devices, constraining the vision screening process. Advance in smartphone camera has enabled novel low-cost ubiquitous vision screening to detect refractive error or ametropia through eye image processing, based on the principle of photorefraction. However, contemporary smartphone-based methods rely heavily on hand-crafted features and sufficiency of well-labeled data. To address these challenges, this paper exploits active learning methods with a set of Convolutional Neural Network features encoding information of human eyes from pre-trained gaze estimation model. This enables more effective training on refractive error detection models with less labeled data. Our experimental results demonstrate the encouraging effectiveness of our active learning approach. The new set of features is able to attain screening accuracy of more than 80% with mean absolute error less than 0.66, meeting the expectation of optometrists for 0.5 to 1. The proposed active learning also requires significantly fewer training samples of 18% in achieving satisfactory performance.