Detection and Classification of Closed Angle Glaucoma Using Optical Coherence Tomography Images

Glaucoma is one of the 3 most important optic nerve diseases that cause vision loss in the world. There are 4 types of glaucoma that develops due to the destruction of the optic nerve, and one of them is closed-angle glaucoma. Closed-angle glaucoma causes an increase in intraocular pressure with the obstruction of drainage channels due to age and triggers glaucoma. In this study, disease classification was made using anterior segment optical coherence tomography (AS-OCT) images of closed-angle glaucoma samples. A total of 1200 ASOCT images were trained with convolutional networks for classification. It supports the use of peripapillary OCT images for the early diagnosis of glaucoma, with a test accuracy of 97.5%, which gives a very good result in peripapillary layer maps of glaucoma. With the developed method, AS-OCT images are aimed to help doctors in the detection and diagnosis of glaucoma