Open Access

ARTICLE

An Optimal Deep Learning Based Computer-Aided Diagnosis System for Diabetic Retinopathy

Phong Thanh Nguyen¹, Vy Dang Bich Huynh², Khoa Dang Vo¹, Phuong Thanh Phan¹, Eunmok Yang^3,*, Gyanendra Prasad Joshi⁴
1 Department of Project Management, Ho Chi Minh City Open University, Ho Chi Minh City, 7000000, Vietnam
2 Department of Learning Material, Ho Chi Minh City Open University, Ho Chi Minh City, 7000000, Vietnam
3 Department of Convergence Science, Kongju National University, Gongju, 32588, South Korea
4 Department of Computer Science and Engineering, Sejong University, Seoul, 05006, South Korea
* Corresponding Author: Eunmok Yang. Email:
(This article belongs to this Special Issue: Intelligent Decision Support Systems for Complex Healthcare Applications)

Computers, Materials & Continua 2021, 66(3), 2815-2830. https://doi.org/10.32604/cmc.2021.012315

Received 25 June 2020; Accepted 29 July 2020; Issue published 28 December 2020

Abstract

Diabetic Retinopathy (DR) is a significant blinding disease that poses serious threat to human vision rapidly. Classification and severity grading of DR are difficult processes to accomplish. Traditionally, it depends on ophthalmoscopically-visible symptoms of growing severity, which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity. This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization (OPSO) algorithm-based Convolutional Neural Network (CNN) Model EOPSO-CNN in order to perform DR detection and grading. The proposed EOPSO-CNN model involves three main processes such as preprocessing, feature extraction, and classification. The proposed model initially involves preprocessing stage which removes the presence of noise in the input image. Then, the watershed algorithm is applied to segment the preprocessed images. Followed by, feature extraction takes place by leveraging EOPSO-CNN model. Finally, the extracted feature vectors are provided to a Decision Tree (DT) classifier to classify the DR images. The study experiments were carried out using Messidor DR Dataset and the results showed an extraordinary performance by the proposed method over compared methods in a considerable way. The simulation outcome offered the maximum classification with accuracy, sensitivity, and specificity values being 98.47%, 96.43%, and 99.02% respectively.

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Keywords

Diabetic retinopathy; convolutional neural network; classification; image processing; computer-aided diagnosis

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Citations