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

https://doi.org/10.1109/access.2024.3432867
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-981-15-5788-0_64openalex-snapshot
Diabetic Retinopathy Detection Using Transfer Learning and Deep Learning
inferredcites →https://doi.org/10.1007/s00521-023-09304-3openalex-snapshot
CTNet: convolutional transformer network for diabetic retinopathy classification
inferredcites →https://doi.org/10.1007/s11042-024-18863-zopenalex-snapshot
Diabetic retinopathy detection and severity classification using optimized deep learning with explainable AI technique
inferredcites →https://doi.org/10.1007/s11517-022-02564-6openalex-snapshot
A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique
inferredcites →https://doi.org/10.1007/s12553-021-00606-xopenalex-snapshot
On the value of deep learning for diagnosing diabetic retinopathy
inferredcites →https://doi.org/10.1007/s12652-020-02727-zopenalex-snapshot
Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification
inferredcites →https://doi.org/10.1016/j.bspc.2020.102115openalex-snapshot
A lightweight CNN for Diabetic Retinopathy classification from fundus images
inferredcites →https://doi.org/10.1016/j.bspc.2024.106346openalex-snapshot
Hybrid hunter-prey ladybug beetle optimization enabled deep learning for diabetic retinopathy classification
inferredcites →https://doi.org/10.1016/j.compbiomed.2013.11.014openalex-snapshot
Detection and classification of retinal lesions for grading of diabetic retinopathy
inferredcites →https://doi.org/10.1016/j.compbiomed.2022.105602openalex-snapshot
Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images
inferredcites →https://doi.org/10.1016/j.compbiomed.2024.108246openalex-snapshot
A novel approach for intelligent diagnosis and grading of diabetic retinopathy
inferredcites →https://doi.org/10.1016/j.compeleceng.2018.07.042openalex-snapshot
Deep convolutional neural networks for diabetic retinopathy detection by image classification
inferredcites →https://doi.org/10.1016/j.compmedimag.2016.08.001openalex-snapshot
Automatic detection of microaneurysms in retinal fundus images
inferredcites →https://doi.org/10.1016/j.jocs.2017.01.002openalex-snapshot
A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions
inferredcites →https://doi.org/10.1016/j.matpr.2022.01.466openalex-snapshot
Binary Classification of DR-Diabetic Retinopathy using CNN with Fundus Colour Images
inferredcites →https://doi.org/10.1016/j.neucom.2020.04.148openalex-snapshot
Deep residual transfer learning for automatic diagnosis and grading of diabetic retinopathy
inferredcites →https://doi.org/10.1080/23311916.2020.1805144openalex-snapshot
Comparative analysis of deep learning methods of detection of diabetic retinopathy
inferredcites →https://doi.org/10.1088/1757-899x/1070/1/012049openalex-snapshot
Detection of diabetic retinopathy using deep learning methodology
inferredcites →https://doi.org/10.1109/access.2021.3109240openalex-snapshot
Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy
inferredcites →https://doi.org/10.1109/access.2022.3177651openalex-snapshot
Texture Attention Network for Diabetic Retinopathy Classification
inferredcites →https://doi.org/10.1109/access.2023.3266511openalex-snapshot
Lightweight EfficientNetB3 Model Based on Depthwise Separable Convolutions for Enhancing Classification of Leukemia White Blood Cell Images
inferredcites →https://doi.org/10.1109/access.2024.3356602openalex-snapshot
Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm
inferredcites →https://doi.org/10.1109/ai4g50087.2020.9311036openalex-snapshot
EdgeAI: Diabetic Retinopathy Detection in Intel Architecture
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/embc.2019.8857160openalex-snapshot
An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification
inferredcites →https://doi.org/10.1109/embc44109.2020.9175664openalex-snapshot
Explainable Diabetic Retinopathy using EfficientNET
inferredcites →https://doi.org/10.1109/ic3ina.2018.8629502openalex-snapshot
Classification of Diabetic Retinopathy Stages using Histogram of Oriented Gradients and Shallow Learning
inferredcites →https://doi.org/10.1109/iccci50826.2021.9402566openalex-snapshot
A Novel Approach for the Early Recognition of Diabetic Retinopathy using Machine Learning
inferredcites →https://doi.org/10.1109/icirca51532.2021.9544730openalex-snapshot
Performance Analysis of Diabetic Retinopathy Classification using CNN
inferredcites →https://doi.org/10.1109/intercon.2017.8079692openalex-snapshot
Automated detection of diabetic retinopathy using SVM
inferredcites →https://doi.org/10.1109/isspit47144.2019.9001846openalex-snapshot
Diabetic Retinopathy Classification Using a Modified Xception Architecture
inferredcites →https://doi.org/10.1109/ngct.2016.7877439openalex-snapshot
Diagnosis of diabetic retinopathy using machine learning classification algorithm
inferredcites →https://doi.org/10.1109/tnb.2018.2840084openalex-snapshot
Microaneurysm Detection Using Principal Component Analysis and Machine Learning Methods
inferredcites →https://doi.org/10.1117/12.2285939openalex-snapshot
Automatic diabetic retinopathy classification
inferredcites →https://doi.org/10.1145/3470976openalex-snapshot
A Convolutional Neural Network Model Using Weighted Loss Function to Detect Diabetic Retinopathy
inferredcites →https://doi.org/10.1155/2007/95103openalex-snapshot
Contributions of Inflammatory Processes to the Development of the Early Stages of Diabetic Retinopathy
inferredcites →https://doi.org/10.1155/2022/4246239openalex-snapshot
Augmentation-Consistent Clustering Network for Diabetic Retinopathy Grading with Fewer Annotations
inferredcites →https://doi.org/10.1186/s12859-023-05293-1openalex-snapshot
Development of revised ResNet-50 for diabetic retinopathy detection
inferredcites →https://doi.org/10.1201/9781003324430-16openalex-snapshot
Automatic Detection of Diabetic Retinopathy to Avoid Blindness
inferredcites →https://doi.org/10.14257/ijgdc.2018.11.1.09openalex-snapshot
Classification of Diabetic Retinopathy Images by Using Deep Learning Models
inferredcites →https://doi.org/10.21203/rs.3.rs-1935432/v1openalex-snapshot
Deep Learning with Class Imbalance for Detecting and Classifying Diabetic Retinopathy on Fundus Retina Images
inferredcites →https://doi.org/10.22214/ijraset.2022.47789openalex-snapshot
An Introduction to Convolutional Neural Networks
inferredcites →https://doi.org/10.32604/cmc.2020.012887openalex-snapshot
Early Detection of Diabetic Retinopathy Using Machine Intelligence Through Deep Transfer and Representational Learning
inferredcites →https://doi.org/10.3390/diagnostics13010124openalex-snapshot
EDLDR: An Ensemble Deep Learning Technique for Detection and Classification of Diabetic Retinopathy
inferredcites →https://doi.org/10.3390/electronics10121369openalex-snapshot
Multi-Scale Feature Fusion with Adaptive Weighting for Diabetic Retinopathy Severity Classification
inferredcites →https://doi.org/10.3390/healthcare11060863openalex-snapshot
Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement
inferredcites →https://doi.org/10.3390/s21113663openalex-snapshot
Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data
inferredcites →https://doi.org/10.3390/s21165283openalex-snapshot
Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model
inferredcites →https://doi.org/10.3390/s22197661openalex-snapshot
Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification
inferredcites →https://doi.org/10.4108/eetpht.9.4335openalex-snapshot
Diabetic Retinopathy Classification Using Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1511.08458openalex-snapshot
An Introduction to Convolutional Neural Networks
inferredcites →https://doi.org/10.5455/aim.2019.27.327-332openalex-snapshot
Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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KAN-Augmented ConvNeXt Model for Robust Multi-Class Diabetic Retinopathy Detection
inferred← citeshttps://doi.org/10.1002/ima.70272openalex-snapshot
A Comprehensive Deep‐Learning Framework Integrating Lesion Segmentation and Stage Classification for Enhanced Diabetic Retinopathy Diagnosis
inferred← citeshttps://doi.org/10.1007/978-3-032-31526-7_33openalex-snapshot
Ensemble Deep Learning for Image Classification of Diabetic Retinopathy
inferred← citeshttps://doi.org/10.1007/978-981-95-1365-9_27openalex-snapshot
An Ensemble Deep Learning Framework for Two-Stage Diabetic Retinopathy Diagnosis and Severity Screening
inferred← citeshttps://doi.org/10.1007/978-981-96-7523-4_27openalex-snapshot
Improved Multimodel Feature Integration for Diabetic Retinopathy Severity Classification
inferred← citeshttps://doi.org/10.1007/s11831-025-10460-zopenalex-snapshot
A Comprehensive Review of Deep Learning Approaches for Automated Detection, Segmentation, and Grading of Diabetic Retinopathy
inferred← citeshttps://doi.org/10.1007/s13410-026-01716-wopenalex-snapshot
CSqueezeNet-DCNN framework for diabetic retinopathy detection and severity level estimation with modified SegNet model
inferred← citeshttps://doi.org/10.1007/s44174-025-00343-zopenalex-snapshot
Real-Time Diabetic Retinopathy Detection Using YOLO-v10 with Nature-Inspired Optimization
inferred← citeshttps://doi.org/10.1016/j.bspc.2026.109555openalex-snapshot
Feature intensity-based vision transformer for accurate detection of vitreous hemorrhage in fundus images
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Intelligent PI control for trajectory regulation in autonomous vehicles using a voting-based ensemble of statistical learning models
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Deep learning-based diabetic retinopathy recognition and grading: Challenges, gaps, and an improved approach — A survey
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Early detection and classification of diabetic retinopathy by transfer learning of NASNet-large and ResNet-50 convolutional neural networks
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EfficientNet and Swin Transformer Fusion for Diabetic Retinopathy Screening
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DFEN: Dual-fusion ensemble network for robust diabetic retinopathy classification from fundus images
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DRCNN-Lesion Proxy: a hybrid CNN with lesion-inspired feature simulation for diabetic retinopathy severity classification
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Task optimized vision transformer for diabetic retinopathy detection and classification in resource constrained early diagnosis settings
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Advanced Agricultural Query Resolution Using Ensemble-Based Large Language Models
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Predictive PID Control for Automated Guided Vehicles Using Genetic Algorithm and Machine Learning
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Improving Inference Time in Diabetic Retinopathy—Recent Trends and Future Directions
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High-Fidelity Remote Sensing-Based Solar Panel Mapping Using a Parameter-Efficient UNet With Dilated Multi-Scale Convolutions and Lightweight Attention
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EfficientNetB0-based Automated Diabetic Retinopathy Classification in Fundus Images
inferred← citeshttps://doi.org/10.1109/icacrs67045.2025.11324180openalex-snapshot
SqueezeCapsNet: A Hybrid SqueezeNet and Capsule Network Model for Early Detection of Diabetic Retinopathy
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An Interpretable Hybrid Deep Learning Framework for Accurate Diabetic Retinopathy Detection Using Grad-CAM
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Detection Of Diabetic Retinopathy Using Deep Learning
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Enhanced Medical Image Analysis for Diabetic Eye Disease Detection Using RAM-Net Architecture
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EyeNet+: A Modified LeNet Architecture for Automated Diabetic Retinopathy Screening
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Explainable Deep Learning-Based Medical Diagnosis Assistant for Multi-Disease Detection and Clinical Decision Support
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Analysis of Gender based Diabetic Retinopathy Detection and Grading Systems
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Detection of Diabetic Retinopathy using Intelligent Gradient Boosted Decision Trees
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A Deep Learning Based Model for Diabetic Retinopathy Detection
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Hybrid Machine Learning Model for Joint Prediction of Diabetic Retinopathy and Nephropathy
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A Feature-Optimized Ensemble Model for Diabetic Retinopathy Detection via CNN and APSO Integration
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ADVANCEMENTS IN AI-DRIVEN DIABETIC RETINOPATHY DETECTION: A SYSTEMATIC SURVEY OF METHODS, CHALLENGES, AND FUTURE DIRECTIONS
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