International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 10 | Oct -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 591 FPGA Implementation of Glaucoma Detection Using Neural Networks Hitesh Shirke 1 , Dr. Nataraj Vijapur 2 1 IV Semester M.Tech. Student, Dept. of Electronics and Communication Engineering. K.L.E. Dr. M.S.Sheshgiri College of Engineering and Technology, Belagum-590008 Karnataka, India-590018 2 Faculty Dept. of Electronics and Communication Engineering. K.L.E. Dr. M.S.Sheshgiri College of Engineering and Technology, Belagum-590008 Karnataka, India-590018 ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Glaucoma is an eye disease that occurs because of the increased IntraOcular Pressure(IOP) which results in damaged optic nerve. Because of glaucoma the optic cup size gets increased and the optic cup to optic disk ratio(CDR) increases. This paper proposes glaucoma disease detection from retinal images using artificial neural network as the classifier. Optic cup area, optic disk area and neuro-retinal rim area are the features that are used for the classification. FPGA implementation of neural network offers both the adaptability in reconfiguration and parallel architecture of neurons. Hence FPGA is better choice for the neural networks instead of DSP or ASIC implementation. The feed forward back propagation algorithm is used for the neural network. Matlab R2015a software is used to extract the features and the neural network is implemented on Spartan 3a FPGA kit. Key Words: Glaucoma, Cup to disk ratio, Neuroretinal rim, Artificial neural network, FPGA implementation 1. INTRODUCTION The human eye is the most important part of the human body. Therefore detection of human eye diseases is always needed. Diseases like glaucoma, cataract, macular degeneration, diabetic retinopathy affect the patientǯs eye. Glaucoma is a very silent disease of the eye. The scary part about this disease is that it is so silent that it normally doesnǯt announce itself. Itǯs a disease which affects the optic nerve of the eye. It causes thinning up and drying up of the optic nerve. This results in visual loss which can be irreversible. Glaucoma can happen in anyone across the population which means any person and age. The easiest way to prevent glaucoma is to get it diagnosed in the very early stages. There is no prevention of glaucoma if you are the person who is going to have glaucoma in your lifetime. Glaucoma is best treated medically using eye drops or using tablets to reduce the pressure inside the eye. This work aims to detect glaucoma using FPGA hardware. The artificial neural network is used as a classifier to detect glaucoma. 1.1 Glaucoma Measurably just about 45 million individuals worldwide have glaucoma and around 79 million individuals are expected to suffer from glaucoma by the year 2020. The optic nerve is in charge of conveying the data seen by eye to the brain. If there should be an occurrence of Glaucoma, optic nerve is harmed and the data got by the brain gets debased henceforth the vision loss. The essential reason behind this harm of the optic nerve is increased IntraOcular Pressure(IOP). Keeping in mind the end goal to keep up a steady eye pressure, human eye ceaselessly delivers aqueous humor, a liquid which moreover continually streams out of the eye. In the event of glaucoma, there is an increase in liquid pressure of the eye on the grounds that the aqueous humor does not stream out of the eye as it ought to be which at last results in damage to the optic nerve fiber. This optic nerve harm can be recognized by Optical Coherence Tomography (OCT) and Heidelberg Retina Tomography (HRT). The cost of glaucoma identification utilizing OCT and HRT is high. Colour Fundus Image (CFI) technique has been broadly used to analyze glaucoma and other visual sicknesses. 1.2 Glaucoma Effects Fig-1: normal fundus image