Table 1

Title	Year	Methodology
Advance Deep Learning Approach For Diabetic Retinopathy Detection And Severity Classification Utilizing Resnet50	2024	ResNET50 model with customized classification block
A Hybrid Neural Network Approach For Classifying Diabetic Retinopathy Subtypes	2024	Hybrid neural network model EfficientNet and Swin Transformer
Hybrid Methods for Fundus Image Analysis for Diagnosis of	2023	GoogLeNet with SVM and ResNet-18 with SVM and Feed-Forward Neural Networks
Diabetic Retinopathy Development Stages Based on
Fusion Features
A Hybrid Deep Learning Metaheuristic Model for Diagnosis of Diabetic Retinopathy	2023	A deep learning model InceptionV3,
		Simulated Annealing and XGBoost model
A Hybrid Technique for Diabetic Retinopathy Detection Based	2023	Binary Dragonfly Algorithm, Sine Cosine Algorithm, SVM classifiers.
on Ensemble-Optimized CNN and Texture Features
Deep Learning Approach for Automatic	2022	Pre trained CNN models, named as VGG-19 and
Microaneurysms Detection		Inception-v3.
Detection of Microaneurysm in Retina Image	2020	Feed forward Neural Networks
using Machine Learning Approach
Adaptive machine learning classification	2020	Pre trained CNN is adapted to get the
for diabetic retinopathy		segment level DRE (Diabetic retinopathy Estimation)
Identification of Diabetic Retinopathy from fundus	2019	The segment based image level pre-trained CNN
images using CNNs

Table 1: Papers Particulars

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Table 1

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Figure 1

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Figure 2

FIGURE 1

Figure 1: Conceptual Diagram for Algorithm of Optimized HHM for DR Detection

FIGURE 2

Figure 2: Data Processing Diagram