Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network

Abstract

Our study provides a practical strategy for addressing imbalanced ophthalmological datasets and has the potential to be applied to other medical images. The developed and deployed CS-ResCNN could serve as computer-aided diagnosis software for ophthalmologists in clinical application.