An Automated Detection and Multi-stage classification of Diabetic Retinopathy using Convolutional Neural Networks
Author:
Affiliation:
1. K.S.Rangasamy College of Technology,Electronics and Communication Engineering,Tiruchengode,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10156870/10156895/10157960.pdf?arnumber=10157960
Reference14 articles.
1. Multi-Stream Deep Neural Network for Diabetic Retinopathy Severity Classification Under a Boosting Framework
2. Uncertainty-Aware Deep Learning Methods for Robust Diabetic Retinopathy Classification
3. Intensity Based Optic Disk Detection for Automatic Diabetic Retinopathy
4. Detection of Diabetic Retinopathy using Machine Learning Algorithm
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1. CGSX Ensemble: An Integrative Machine Learning and Deep Learning Approach for Improved Diabetic Retinopathy Classification;International Journal of Electrical and Electronics Research;2024-06-28
2. Enhanced Diabetic Retinopathyanalysis: Unet-Based Lesion Segmentation Coupled with Mobilenetv2 for Feature Extraction and Efficientnetb0 for Classification;2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS);2024-03-14
3. Enhancing Diabetic Retinopathy Image Classification using CNN, Resnet, and Googlenet Models with Z-Score Normalization and GLCM Feature Extraction;2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE);2024-01-24
4. Diabetic Retinopathy Classification Using PSOSVM Based Deep Learning Model;2023 Seventh International Conference on Image Information Processing (ICIIP);2023-11-22
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