A Semi-supervised Generative Adversarial Network for Retinal Analysis from Fundus Images

Abstract

Retinal disorders are the prominent diseases causing visual impairments to a large population across the globe. A prompt diagnosis can address this problem to a large extend. Moreover, AI-enabled devices help ophthalmologists in the timely diagnosis of the disorders and initiate appropriate treatment. In this work, we develop a GAN based semi-supervised model to extract the prominent retinal structures and classify the fundus images as normal or abnormal using data from multiple repositories. The dice coefficient of 0.9 across various datasets affirms the good performance on source independent data. Such a model can be extended to incorporate additional features and be integrated into the ophthalmoscopes for quick retinal examination through telemedicine. © 2021, Springer Nature Singapore Pte Ltd.