Optical Cup and Disc Segmentation using Deep Learning Technique for Glaucoma Detection

Abstract

The optic nerve damaging condition called Glaucoma. This disease is increment at an alarming rate. By the end of the 2044 there is possibility that across 111.8 million populations will be influenced by glaucoma. It is a neurodegenerative disease. If intravascular pressure is increases, optic nerve of the eye gets damage. This damage may cause permanent or total blindness in person. The Glaucoma is examined by an experienced ophthalmologist on the retinal part of the eye. This process required excessive equipment, experienced medical practitioners and also it take more time to work out manually. After considering this problem there is an extreme requirement of developing an automatic system which will effectively and automatically work properly in lack of any professional doctor and it should also take less time. Lots of different parameters are available to detect glaucoma but thebest parameter is to find out optical cup-to-disc-ratio. To increase or to enhance the precision and accuracy of the result, cup to disc value is needed to find CDR value. In order to detect glaucoma, automatic separation of the OC and DC is very essential to avoid any error. We use deeplabv3 architecture to perform segmentation of optic disc and cup and classification is done using ensemble machine learning. This proposes research achieve intersection over union (IOU) scores, 0.9423 for optic disc and 0.9310 for optic cup. We perform testing on globally accessible data-sets i.e. DRISHTI, ORIGA, and RIMONE with accuracy of 93%, 91% and 92% respectively