Semi-supervised Blindness Detection with Neural Network Ensemble

Diabetic retinopathy (DR), a common complication of diabetes mellitus, is a major cause of visual loss among the working-age population. Since DR vision loss is irreversible, early detection of DR is crucial for preventing vision loss in patients. However, manual detection of DR remains time costly and inefficient. In this paper, an ensemble of 6 pre-trained neural networks (including EfficientNets, ResNet, and Inception) are combined. The compatibility of different networks is tested by creating different combinations of networks and evaluating their relative performance. Pseudo-labelling is used to further increase accuracy. With a limited training data set of only 5592 images, the final neural network ensemble achieved an accuracy of 0.864.