Faster R-CNN and DenseNet Regression for Glaucoma Detection in Retinal Fundus Images

Glaucoma is one of the main retinal diseases. Glaucoma affects older people more often, and it can lead to vision loss. Until now there is no medicament for Glaucoma, but early detection is important, wherein it can limit the increase of vision loss or blindness. In this paper, we propose a deep learning approach based on two steps for Glaucoma detection in retinal fundus images. In the first step, we use a faster region proposal neural network (RCNN) to detect the optical disc (OD). Then in a second step, we train a regression network to estimate the cup-to-disc ratio (CDR) by analyzing reign around the detected OD. Experimental results of this method are demonstrated on the MESSIDOR and Magrabi datasets.