Semi-supervised Transfer Learning for Convolutional Neural Networks for Glaucoma Detection

Abstract

Convolutional neural network (CNN) can be applied in glaucoma detection for achieving good performance. However, its performance depends on the availability of a large number of the labelled samples for its training phase. To solve this problem, this paper present a semi-supervised transfer learning CNN model for automatic glaucoma detection based on both labeled and unlabeled data. First, a pre-trained CNN from non-medical data is fine-tuned and trained in a supervised fashion using the labeled data. The self-learning approach is then used to predict the labels for the unlabeled data and utilize it for training. The experimental results on the RIM-ONE database demonstrate the effectiveness of the proposed algorithm despite the lack of initial labeled samples.