An Improved Semi-Supervised Learning Method on Cataract Fundus Image Classification

Abstract

The fundus is part of the eye, and any partof the fundus is referred to as a fundus disease. In recentyears, the incidence of eye diseases has increased. Accordingto some statistics, cataracts are the most common cause ofblindness. Considering that the fundus image is one of themost important medical references that contributes to diagnosis, cataract classification and grading based on fundus images aresignificant. The fundus image analysis was used to simulate thework of the ophthalmologist's fundus image, and the cataractdetection and grading activities were examined. This is the useof machine learning to perform fundus image classification; inpractice, label samples tend to cause large losses during machinelearning, so the number of label samples is usually limited. For the damage effects of cataract disease, this paper proposesan improved semi-supervised learning method to acquire someadditional information from unlabeled cataract fundus imagesto improve the accuracy of the basic model for training onlythe marker images. In this proposed approach, we focus onstrategies for updating instance weights and combining severalbinary classifiers into one powerful multi-classifier. First, weadjust the original fundus image and enhance it with a histogramequalization method. Second, we extracted three image features(textures, wavelets, and sketches) from the enhanced fundusimage [1]. Third, we train a semi-supervised model on threeimage features. Finally, we combine several binary classifiersinto one powerful multi-classifier. The cataract fundus imagewill be divided into normal, mild, moderate and severe. Throughexperiments, the overall accuracy of the four categories on thetest data set is about 88.60%, 1.1% higher than Qiao's 87.52%, and 2.6% higher than the Song's 86.0% [2].