Glaucoma detection using cup to disc ratio and artificial neural networks - R., Gayathri; P. V., Rao (International Journal of Engineering and Technology, 2017)
Now-a-days, the most commonly predicted eye disease in human beings is glaucoma; loss of vision gradually may turn into blindness. Advanced image handling methods empower osteopathic specialist to distinguish and treat a few eye infections like diabetic retinopathy and glaucoma. The pressure in the optic nerve of the eye may lead to get affected by glaucoma, which is most regular reason for visual deficiency of the peoples, if not treated appropriately at early stage. The main objective of this paper is the detection of glaucoma and classifies the disease based on its severity using artificial neural network. In this paper mainly focused on pre -processing of retinal fundus images for improving the quality of detection and easy to further handling. The simulation results to obtain using MATLAB for the better accuracy in detecting glaucoma for abnormality using Cup to Disc ratio of retinal fund us images.Â