This paper is published in Volume-3, Issue-4, 2017
Area
Deep Learning
Author
Vishakha Chandore
Co-authors
Shivam Asati
Org/Univ
IIT, Delhi, India
Pub. Date
16 August, 2017
Paper ID
V3I4-1321
Publisher
Keywords
Diabetic Retinopathy, Fundus Images, Neural Network, Macula, Optic Disc.

Citationsacebook

IEEE
Vishakha Chandore, Shivam Asati. Automatic Detection of Diabetic Retinopathy using Deep Convolutional Neural Network, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Vishakha Chandore, Shivam Asati (2017). Automatic Detection of Diabetic Retinopathy using Deep Convolutional Neural Network. International Journal of Advance Research, Ideas and Innovations in Technology, 3(4) www.IJARIIT.com.

MLA
Vishakha Chandore, Shivam Asati. "Automatic Detection of Diabetic Retinopathy using Deep Convolutional Neural Network." International Journal of Advance Research, Ideas and Innovations in Technology 3.4 (2017). www.IJARIIT.com.

Abstract

The purpose of this project is to design an automated and efficient solution that could detect the symptoms of DR from a retinal image within seconds and simplify the process of reviewing and examination of images. Diabetic Retinopathy (DR) is a complication of diabetes that is caused by changes in the blood vessel of the retina and it is one of the leading causes of blindness in the developed world. Currently, detecting DR symptoms is manual and time-consuming process. Recently, fully-connected and convolutional neural networks have been trained to achieve state-of-the-art performance on a wide variety of tasks such as speech recognition, image classification, natural language processing, and bioinformatics. In our approach, we trained a deep Convolutional Neural Network model on large dataset consisting around 35,000 images and used dropout layer techniques to achieve higher accuracy.
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