Diabetic Retinopathy Detection Using Neural Network
Vijay Kumar Gurani1, Abhishek Ranjan2, Chiranji Lal Chowdhary3

1Vijay Kumar Gurani, Department of Computer Scienct, Karnataka University Dharwad, India.
2Abhishek Ranjan, Dean and Head of Institution, Botho University, Lesotho.
3Chiranji Lal Chowdhary, Department of Information Technology, VIT Vellore, India.

Manuscript received on 15 August 2019 | Revised Manuscript received on 21 August 2019 | Manuscript published on 30 August 2019 | PP: 2936-2940 | Volume-8 Issue-10, August 2019 | Retrieval Number: J11050881019/2019©BEIESP | DOI: 10.35940/ijitee.J1105.0881019
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Now-a-days diabetics are affecting many people and it causes an eye disease called “diabetics retinopathy” but many are not aware of that, so it causes blindness. Diabetes aimed at protracted time harms the blood vessels of retina in addition to thereby affecting seeing ability of an individual in addition to leading to diabetic retinopathy. Diabetic retinopathy is classified hooked on twofold classes, non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). Finding of diabetic retinopathy in fundus imaginary is done by computer vision and deep learning methods using artificial neural networks. The images of the diabetic retinopathy datasets are trained in neural networks. And based on the training datasets we can detect whether the person has (i)no diabetic retinopathy, (ii) mild non-proliferative diabetic retinopathy, (iii) severe non-proliferative diabetic retinopathy and (iv) proliferative diabetic retinopathy.
Index Terms: Diabetic, Retinopathy, Detection, Neural Network.

Scope of the Article: Classification