An Optimal Deep Learning Based Computer-Aided Diagnosis System for Diabetic Retinopathy Phong Thanh Nguyen 1 , Vy Dang Bich Huynh 2 , Khoa Dang Vo 1 , Phuong Thanh Phan 1 , Eunmok Yang 3,* and Gyanendra Prasad Joshi 4 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: emyang@kongju.ac.kr Received: 25 June 2020; Accepted: 29 July 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 ophthalmos- copically-visible symptoms of growing severity, which is then ranked in a step- wise 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 EOP- SO-CNN in order to perform DR detection and grading. The proposed EOP- SO-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. Keywords: Diabetic retinopathy; convolutional neural network; classification; image processing; computer-aided diagnosis 1 Introduction In recent years, Diabetic Retinopathy (DR) is one of the major problems faced by many individuals that primarily affect the human vision. There are few ophthalmology-related diseases like diabetes, hypertension, and arteriosclerosis, which are considered to be the major reason behind blindness. Many professionals examined the modification of vascularmorphology by portioning the retinal vessels. Thus, the This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computers, Materials & Continua DOI:10.32604/cmc.2021.012315 Article ech T Press Science