# Classification and Segmentation of Diabetic Retinopathy: A Systemic Review
## 7 Benchmark Datasets
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref \#** & **Year** & **Methodology** &
\begin{tabular}{c} **Deep Feature** \\ **Extraction Method** \\ \end{tabular} & **Classifiers** & **Datasets** & **Results** \\ \hline
[154] & 2022 &
\begin{tabular}{c} CLAHE, Median Filter, Gaussian Filter, \\ Min-Max Normalization, RBT, KGW, \\ FRO, CNN, KGW-FFO \\ \end{tabular} & IGWO-FFO & CNN Softmax & APTOS2019 & ACC = 94.11\% \\ \hline
[155] & 2022 &
\begin{tabular}{c} KNN, XGBOT, SVM, PCA, AHo, DT, \\ DNN-PCA-HHO \\ \end{tabular} & DNN-PCA-HHO & DNN-PCA-HHO & KNN, XGBOT, \\ SVM & UCI & ACC = 97\% \\ \hline
[156] & 2022 & \begin{tabular}{c} CNN, EyeNet, DenseNet E-DenseNet, \\ Average Pooling (GAP) \\ \end{tabular} & E-DenseNet & Softmax & \
\begin{tabular}{c} IDRiD, Messidor, \\ EyePACS, \\ APTOS 2019 \\ \end{tabular} &
\begin{tabular}{c} ACC = 93\%, \\ ACC = 91\%, \\ ACC = 96.80\%, \\ ACC = 84\% \\ \end{tabular} \\ \hline
[157] & 2022 &
\begin{tabular}{c} CLAHE, Weighted Gaussian Blur, \\ Average Pooling, Augmentation, VGGNet \\ \end{tabular} & VGGNet & Average Pooling & EyePACS & ACC = 97.10\% \\ \hline
[48] & 2021 &
\begin{tabular}{c} Faster-RCNN, DenseNet-65, Gaussian \\ Filter, VGG, AlexNet, ResNet \\ \end{tabular} & Faster-RCNN & DenseNet-65 & Kaggle, APTOS & ACC = 97.20\% \\ \hline
[158] & 2021 & \begin{tabular}{c} Random Forest, ResNet-50, MobileNet, \\ VGG16, VGG-19, Xception, Inception-V3 \\ \end{tabular} & ResNet-50 & Random Forest & \
\begin{tabular}{c} Messidor-2, \\ EyePACS \\ \end{tabular} &
\begin{tabular}{c} ACC = 96\%, \\ ACC = 75.09\% \\ \end{tabular} \\ \hline
[159] & 2021 & \begin{tabular}{c} Inception-V3, ResNet101, VGG-19, Naive \\ Bayes, KNN, SVM \\ \end{tabular} & \begin{tabular}{c} Inception-V3, \\ ResNet101, Vgg19 \\ \end{tabular} & SVM & \
\begin{tabular}{c} Kaggle, \\ Messidor-2, IDRiD \\ \end{tabular} &
\begin{tabular}{c} ACC = 97.98\%, \\ SF = 97.60\%, \\ SF = 99.30\% \\ \end{tabular} \\ \hline
[160] & 2021 & \
\begin{tabular}{c} CNN, SqueezeNet, ResNet-50, \\ Inception-V3, DFTSA-Net, CLAHE \\ \end{tabular} & DFTSA-Net & Softmax & IDRiD &
\begin{tabular}{c} ACC = 96.80\%, \\ SF = 97.50\%, \\ SF = 95.50\% \\ \end{tabular} \\ \hline
[161] & 2020 & \
\begin{tabular}{c} DNN, KNN, SVM, MLP, VGG16, \\ Xception, ResNetV2, NASNET \\ \end{tabular} &
\begin{tabular}{c} VGG16, Xception, \\ Classifier, Decision \\ Tree, Logistic \\ Regression, MLP \\ \end{tabular} & APTOS 2019 & ACC = 97.41\% \\ \hline
[163] & 2020 & \begin{tabular}{c} CNN, Inception-V3, Softmax, GMM, ALR \\ \end{tabular} & Inception-V3, & Softmax & \
\begin{tabular}{c} e-Ophtha, \\ DIARETDB1 \\ \end{tabular} &
\begin{tabular}{c} ACC = 98.43\%, \\ ACC = 98.91\% \\ \end{tabular} \\ \hline
[164] & 2020 & \
\begin{tabular}{c} CNN, CLAHE, ResNet-50, SVM, KNN, \\ Random Forest, XGBoost \\ \end{tabular} & ResNet-50 &
\begin{tabular}{c} SVM, KNN, Random \\ Forest, XGBoost \\ \end{tabular} & DIARETDB1 & ACC = 99\% \\ \hline
[165] & 2020 &
\begin{tabular}{c} RNN, Morphological Operation, RPN, \\ Softmax, Rounding Box \\ \end{tabular} & Faster-RCNN & Softmax & Messidor & ACC = 96.80\% \\ \hline
[166] & 2019 & \
\begin{tabular}{c} Cropping, Resizing, Histogram \\ Equalization, CNN, VGG-16, SqueezeNet, \\ AlexNet \\ \end{tabular} & Convolution Layers &
\begin{tabular}{c} VGG-16, SqueezeNet, \\ AlexNet \\ \end{tabular} & Messidor & ACC = 98.15\% \\ \hline
[167] & 2019 & \begin{tabular}{c} CNN, Deep CNN, Inception-V3, \\ Dense-169, ResNet-50, \\ Xception, Dense-121, \\ \end{tabular} & \
\begin{tabular}{c} Dense-121, \\ Inception-V3, \\ Dense-169, Xception \\ ResNet-50 \\ \end{tabular} &
\begin{tabular}{c} Binary Classification, \\ Multi-Class \\ Classification \\ \end{tabular} & Kaggle & SP = 99\% \\ \hline
[168] & 2018 & PCA, Bag of Words (Bow), CNN, AlexNet & AlexNet & SVM & SD-OCT &
\begin{tabular}{c} ACC = 96.80\% \\ SF = 93.75\% \\ SP= 100\% \\ \end{tabular} \\ \hline
[169] & 2018 &
\begin{tabular}{c} CNN, AlexNet DNN, SVM, PCA, LDA, \\ SIFT, Histogram Equalization, GMM \\ \end{tabular} & AlexNet DNN & SVM & Kaggle & ACC = 97.93\% \\ \hline
[170] & 2017 & \begin{tabular}{c} Augmentation, Image Transformation, \\ Contrast Enhancement, Decision Tree \\ \end{tabular} & Residual Network & Decision Tree & \
\begin{tabular}{c} Messidor-2, \\ e-Ophtha, \\ EyePACS \\ \end{tabular} &
\begin{tabular}{c} SP = 87\%, \\ SP = 94\%, \\ SP = 98\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}