Advanced Color Fundus Photography using Deep Learning for Screening Glaucoma in Low Resource Setting

Abstract

We introduce an advanced color fundus photography using deep learning (DL) architecture for screening glaucoma in low resource setting. The proposed DL architecture is based on a convolutional neural network and trained using clinical image data from color fundus photography and optical coherence tomography. Customized hand-held device integrated with DL model detect and quantify glaucomatous damage in fundus photograph. In validation study, our approach improves the screening capability which cannot be achieved by retinal fundus photography alone. This low-cost handy device with fast-feedback software would be very adequate tool to screen glaucoma in low resource setting.