Enhancing Early-Stage Diabetic Retinopathy Detection Using a Weighted Ensemble of Deep Neural Networks-Reference-Cited by-同舟云学术

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Enhancing Early-Stage Diabetic Retinopathy Detection Using a Weighted Ensemble of Deep Neural Networks

Published:2024 Issue: Volume:12 Page:113565-113579
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Nazir Kinza¹^ORCID,Kim Jisoo²,Byun Yung-Cheol³^ORCID

Affiliation:

1. Department of Electronic Engineering, Institute of Information Science and Technology, Jeju National University, Jeju-si, South Korea

2. Faculty of Software (Artificial Intelligence Major), College of Engineering, Jeju National University, Jeju-si, South Korea

3. Department of Computer Engineering (Major of Electronic Engineering), Institute of Information Science and Technology, Jeju National University, Jeju-si, South Korea

Funder

“Regional Innovation Strategy (RIS)” through the National Research Foundation of Korea

Ministry of Education

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10608114.pdf?arnumber=10608114

Reference55 articles.

1. An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification

2. EDLDR: An Ensemble Deep Learning Technique for Detection and Classification of Diabetic Retinopathy

3. Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data

4. Classification of Diabetic Retinopathy Images by Using Deep Learning Models

5. Diagnosis of diabetic retinopathy using machine learning classification algorithm

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