Faster R-CNN classification for the recognition of glaucoma

Abstract

Abstract Glaucoma is an optic neuropathy characterized by progressive degeneration of retinal ganglion cells. The early identification of Glaucoma is extremely important as it is detrimental to one’s blindness. In this paper, we present the identification of glaucoma using faster R-CNN which is one of the most well-known object detection neural networks. The proposed method uses artificial intelligence and enhanced deep learning to detect Glaucoma. Faster R-CNN comprises two modules, the region proposal network (RPN), in which the region of object is distinguished on the picture, and a network that enables to classify the objects in the proposed region. We have accomplished the finest output by applying a transfer learning scheme with ResNet50 and VGG16. Using ResNet we have detected Glaucoma with up to 96% accuracy. The test results obtained by making use of two unique publicly available data sets DRISHTI_GS and ORIGA with 751 images demonstrate that this arrangement can be a significant alternative for the computer design aid framework for the large-scale screening programs of glaucoma detection.