# Classification and Segmentation of Diabetic Retinopathy: A Systemic Review
## 3 Preprocessing
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref \#** & **Year** & **Methodology** & **Preprocessing Methods** & **Datasets** & **Results** \\ \hline
[22] & 2022 & CLAHE, Contrast-Enhanced Canny & CLAHE & Messidor, Kaggle &
\begin{tabular}{c} Accuracy (ACC) = 98.50\%, Sensitivity \\ (SF) = 98\%, Specificity (SF) = 98\%, \\ ACC = 98\%, SF = 98.70\%, \\ SP = 97.80\% \\ \end{tabular} \\ \hline
[23] & 2022 &
\begin{tabular}{c} Deformable Transformation, \\ B-Spline Registration, \\ Xception, Inception-V3, \\ DenseNet-121, ResNet-50 \\ \end{tabular} & Deformable Registration & APTOS & ACC = 85.28\% \\ \hline
[24] & 2022 &
\begin{tabular}{c} Gaussian Scale Space (GST), Krizhevsky \\ Augmentation, Weighted \\ Gaussian Blur, NLMD \\ \end{tabular} & Weighted Gaussian Blur & Kaggle & ACC = 97.10\% \\ \hline
[38] & 2022 &
\begin{tabular}{c} Morphological Gradient, \\ Atom Search Optimization \\ \end{tabular} & Morphological Gradient & Kaggle & ACC= 99.81\% \\ \hline
[39] & 2022 & \
\begin{tabular}{c} MTRO, WGA, \\ Grayscale Conversion, \\ Shade Correction \\ \end{tabular} &
\begin{tabular}{c} Grayscale Conversion, \\ Shade Correction \\ \end{tabular} & DRIVE & ACC = 95.42\%, SF = 93.10\%, SF = 93.20\% \\ \hline
[40] & 2022 &
\begin{tabular}{c} U-Net, \\ Gaber Filter, Median Filter \\ \end{tabular} & Median Filter & DIARETDB0, & ACC = 95.90\% \\ \hline
[41] & 2022 &
\begin{tabular}{c} Adaptive Histogram Equalization Filter, \\ CLAHE, Gamma Correction, \\ Morphological Reconstruction, \\ K-Means Clustering \\ \end{tabular} & Adaptive Histogram Equalization Filter, & Messidor & ACC = 97.60\%, \\  & & & & \\  & & & & \\ \hline
[42] & 2022 & \begin{tabular}{c} Data Augmentation, \\ Morpho-Inception-V4, Stochastic Gradient \\ Descent (SGD) \\ \end{tabular} & \
\begin{tabular}{c} Data Augmentation, \\ Cropping, Flipping, \\ Rotation \\ \end{tabular} & Messidor-2 &
\begin{tabular}{c} ACC = 99.20\%, \\ SF = 92.50\%, \\ SP = 96.10\% \\ \end{tabular} \\ \hline
[43] & 2021 &
\begin{tabular}{c} Blurring, \\ Bounding Box, \\ Inception-Resnet \\ \end{tabular} & Bounding Box & Messidor, APTOS & ACC = 72.33\%, \\ \hline
[44] & 2021 & CLAHE, Green Channel, Erosion, Dilation, \\  & & & & & \\  & & & & DRIGHT1-GS & SF = 94.44\%, \\ \hline
[45] & 2021 & \
\begin{tabular}{c} Grayscale Conversion, Binarization, \\ Adaptive histogram Equalization, CLAHE \\ Canny Edge Detection, \\ Green Channel, Dilation, Erosion \\ \end{tabular} &
\begin{tabular}{c} Adaptive Histogram \\ Equalization, CLAHE \\ \end{tabular} & DIARETDB0, & ACC = 87.20\%, \\  & & & & & \\ \hline
[46] & 2021 &
\begin{tabular}{c} Gaussian Blur, \\ Data Augmentation, \\ Global Average Pooling 2D, \\ Adam Optimization \\ \end{tabular} & Gaussian Blur & Kaggle & ACC = 90\% \\ \hline
[47] & 2021 &
\begin{tabular}{c} Annotation's Bounding Box, \\ Region of Interest, \\ Gaussian Filter, Cropping, \\ Contrast Variations \\ \end{tabular} & Gaussian Filter, Cropping & Kaggle, APTOS & ACC = 97.20\% \\ \hline
[48] & 2021 &
\begin{tabular}{c} U-Net, OTSU, \\ Region of Interest, \\ Gaussian Filter, CLAHE \\ \end{tabular} & Gaussian Filter, CLAHE & IDRID & SF = 87.55\% \\ \hline
[11] & 2021 &
\begin{tabular}{c} UNet, MResUNet, CLAHE, Cropping, \\ Patching, \\ Weighted Cross-Entropy Loss Function, \\ Mathematical Morphology \\ \end{tabular} & CLAHE, \\  & & & & \\ \hline
[50] & 2021 &
\begin{tabular}{c} Green Channel, CLAHE, Morphological \\ Operation, Thresholding \\ \end{tabular} & CLAHE, Morphological & IDRID & ACC = 83.84\% \\ \hline
[51] & 2021 &
\begin{tabular}{c} Gabor Filter, SVM, \\ Candidate Region \\ \end{tabular} & Gabor Filter & IDRID & ACC = 80.80\%, \\  & & & & \\ \hline
[52] & 2021 &
\begin{tabular}{c} High-Pass Filter, \\ Morphological Operations, Top-Hat Filter, \\ Operations, \\ Top-Hat Filter \\ \end{tabular} & DIARETDB 0, & ACC = 94.19\%, \\  & & & & \\ \hline
[53] & 2021 &
\begin{tabular}{c} Channel Splitting, Blue Channel \\ Hue Saturation Value (HSV), \\ Patch Segmentation, Crayscale \\ Conversion, SVM \\ \end{tabular} & Grayscale Conversion, \\  & & & & \\ \hline
[55] & 2020 &
\begin{tabular}{c} Threshold, Contrast-Enhanced, Adaptive \\ Average Filter, Meta-Heuristic Algorithm \\ (FF-CSO), Deep CNN, RGB to Lab, \\ Histogram Equalization, Cover \\ \end{tabular} & RGB to Lab, \\  & & & & \\ \hline \end{tabular}
\end{table}