Open Access

ARTICLE

Intelligent Prediction Approach for Diabetic Retinopathy Using Deep Learning Based Convolutional Neural Networks Algorithm by Means of Retina Photographs

G. Arun Sampaul Thomas¹, Y. Harold Robinson², E. Golden Julie³, Vimal Shanmuganathan⁴, Seungmin Rho⁵, Yunyoung Nam^6,*

1 CSE Department, J.B. Institute of Engineering and Technology, Hyderabad, 500075, India
2 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India
3 Department of Computer Science and Engineering, Anna University Regional Campus, Tirunelveli, 627007, India
4 Department of Information Technology, National Engineering College, Kovilpatti, 628503, India
5 Department of Software, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul, 05006, Korea
6 Department of Computer Science and Engineering, Soonchunhyang University, Asan, 31538, Korea

* Corresponding Author: Yunyoung Nam. Email:

(This article belongs to the Special Issue: Deep Learning Trends in Intelligent Systems)

Computers, Materials & Continua 2021, 66(2), 1613-1629. https://doi.org/10.32604/cmc.2020.013443

Received 06 August 2020; Accepted 15 September 2020; Issue published 26 November 2020

Abstract

Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed, leak fluid and vision impairment. Symptoms of retinopathy are blurred vision, changes in color perception, red spots, and eye pain and it cannot be detected with a naked eye. In this paper, a new methodology based on Convolutional Neural Networks (CNN) is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate diagnoses. The CNN model is trained by different images of eyes that have retinopathy and those which do not have retinopathy. The fully connected layers perform the classification process of the images from the dataset with the pooling layers minimize the coherence among the adjacent layers. The feature loss factor increases the label value to identify the patterns with the kernel-based matching. The performance of the proposed model is compared with the related methods of DREAM, KNN, GD-CNN and SVM. Experimental results show that the proposed CNN performs better.

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