# Classification and Segmentation of Diabetic Retinopathy: A Systemic Review
## 4 Segmentation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref \#** & **Year** & **Methodology** & **Segmentation Techniques** & **Datasets** & **Results** \\ \hline \multirow{3}{*}{[67]} & \multirow{3}{*}{2022} & Adaptive Active Contour, Obs & Adaptive Active Contour, & \multirow{3}{*}{IDBiD} & \multirow{3}{*}{ACC = 60\%} \\  & & Median Filtering, Open-Close Watershed & Watershed Transform, & \multirow{3}{*}{OSu Thresholding} & \\  & & Transform, GLM, ROI, LIFT & & & \\ \hline \multirow{3}{*}{[68]} & \multirow{3}{*}{2021} & MSNet, MS-EfficientNet, U-Net, & \multirow{3}{*}{MSRNet, U-Net} & e\_ophtha\_MAs & SF = 71.50\% \\  & & Adam Optimizer & & & \\ \hline \multirow{3}{*}{[69]} & \multirow{3}{*}{2021} & EAD-Net, U-Net, CAM, PAM & EAD-Net, U-Net & e\_ophtha\_EX, DRiD, & \multirow{3}{*}{ACC = 97\%, ACC = 84.86} \\  & & & local dataset & & \\ \hline \multirow{3}{*}{[70]} & \multirow{3}{*}{2021} & U-Net, Model-Driven Bubble Approach, & \multirow{3}{*}{U-Net} & \multirow{3}{*}{Messidor} & \multirow{3}{*}{ACC = 91\%} \\  & & Hough Transform, IRBIF Illumination & & & \\  & & Correction, Logarithmic Transformation & & & \\ \hline \multirow{3}{*}{[72]} & \multirow{3}{*}{2021} & Region Growing, Genetic Algorithm (GA), & \multirow{3}{*}{Region Growing} & Local Dataset & SF = 78\% \\  & & FCM, Clustering Method, K-Means & & & \\ \hline \multirow{3}{*}{[60]} & \multirow{3}{*}{2020} & Watershed Transform, Mathematical & \multirow{3}{*}{Watershed Transform} & DIARETDB0, & \multirow{3}{*}{SF = 87\%, SP = 93\%} \\  & & Morphology Operation, CLAHE, RBF- NN, & & & \\ \hline \multirow{3}{*}{[73]} & \multirow{3}{*}{2020} & Regional Minima & \multirow{3}{*}{Image-Adapted} & \multirow{3}{*}{e\_ophtha\_MAs, } & \multirow{3}{*}{SF = 64\%, } \\  & & Land Filter, De-Ning Techniques, & & & \\ \cline{3-3}  & & Image-Adapted Threshold, Region Growing & & & \\ \cline{3-3}  & & Non-Maximum Suppression (NMS) & & & \\ \hline \multirow{3}{*}{[74]} & \multirow{3}{*}{2020} & U-Net, ResNet34, Initialized to Convolution & \multirow{3}{*}{U-Net} & DRiD, & \multirow{3}{*}{ACC = 99.88\%, ACC = 99.98\%, } \\  & & NN Resize (ICNR) & & & \\ \hline \multirow{3}{*}{[75]} & \multirow{3}{*}{2020} & Region Growing, Gaussian and Gabor Filters, & \multirow{3}{*}{U-Nets Clustering, } & \multirow{3}{*}{20 for Footnote 8: \multirow{3}{*}{[76]} } \\  & & Histogram Equalization, Grayscale (W), COM, Histogram (H), RLM, LIFT, SLg, Multi-Layer & & & \\ \cline{3-3}  & & Perceptron (MLP) & & \\ \hline \multirow{3}{*}{[76]} & \multirow{3}{*}{2020} & Region Growing, Ellipse Fitting, Green & \multirow{3}{*}{Ousu Thresholding, } & \multirow{3}{*}{DIARETDB1, } \\  & & Channel, Morphological Distation Operation, & & & \\ \cline{3-3}  & & Otsu Thresholding, Morphological Operation & & & \\ \hline \multirow{3}{*}{[77]} & \multirow{3}{*}{2020} & Deep CNN, DeepLabV3, Segnet, Conditional & \multirow{3}{*}{DeepLabV3, Segnet} & \multirow{3}{*}{IDRID} & \multirow{3}{*}{ACC = 88\%} \\  & & Random Field (CRF) & & & \\ \hline \multirow{3}{*}{[78]} & \multirow{3}{*}{2019} & U-Nets, LocalNet, GlobalNet, Fusion & \multirow{3}{*}{U-Nets} & \multirow{3}{*}{ISBI 2018} & \multirow{3}{*}{ACC = 89\%} \\  & & Module, Data Augmentation, Concatenate, & & & \\ \cline{3-3}  & Global Supervision, Local Supervision & & & \\ \hline \multirow{3}{*}{[79]} & \multirow{3}{*}{2019} & CNN, ResNet-50, Discriminative Restricted & \multirow{3}{*}{CNN} & DIARETDB1, & \multirow{3}{*}{ACC = 90.60\%, ACC = 89.10\%} \\  & & Boltzmann Machines, OPF, KNN, SVM & & & \\ \hline \multirow{3}{*}{[80]} & \multirow{3}{*}{2019} & Random Forest Classifier, K-Means, Naive & \multirow{3}{*}{K-Means, Morphological } & \multirow{3}{*}{DIARETDB0, } \\  & & Conversion, Gamma Correction, & & \\ \cline{3-3}  & Region-Based Features & & & \\ \hline \multirow{3}{*}{[81]} & \multirow{3}{*}{2019} & U-Net, HEDNet, HEDNet+cGAN, & \multirow{3}{*}{U-Net, HEDNet, } \\  & & Conditional Generative Adversarial Network & & \\ \cline{3-3}  & & (cGAN), PatchGAN, VGG16 Weighted & \multirow{3}{*}{HEDNet+cGAN} & \multirow{3}{*}{IDRiD} & \multirow{3}{*}{Precision = 84.05\%} \\ \cline{3-3}  & & Binary Cross-Entropy, loss, CLAHE, & & \\ \cline{3-3}  & & Bilateral Filter & & \\ \hline \multirow{3}{*}{[82]} & \multirow{3}{*}{2019} & \multirow{3}{*}{Deep-CNN, Binary Cross Entropy, VGG16} & \multirow{3}{*}{Deep-CNN} & \multirow{3}{*}{IDRiD, Drishti-GS} \\  & & CNN-Based U-Net, Bootstrap & & \\ \cline{3-3}  & & Cross-Entropy, Instance Normalization, & & \\ \cline{3-3}  & & Atrous Convolutions & & \\ \hline \multirow{3}{*}{[83]} & \multirow{3}{*}{2018} & CNN, GoogLeNet, Inception-V3, VGG16, & \multirow{3}{*}{CNN} & \multirow{3}{*}{Kaggle, e\_ophtha} & \multirow{3}{*}{ACC = 98\%, ACC = 95\%} \\  & & ResNet, AlexNet, Sliding Windows & & \\ \hline \multirow{3}{*}{[84]} & \multirow{3}{*}{2018} & Dynamic Decision Thresholding, Adaptive & \multirow{3}{*}{DIARETDB1, STARE, } & \multirow{3}{*}{ACC = 93.40\%, ACC = 93.4\%, } \\  & & Contrast Enhancement, Canny Edge & & & \\ \cline{3-3}  & Detection, Circular Hough Transform, & & & \\ \cline{3-3}  & Morphological Filling & & & \\ \hline \multirow{3}{*}{[85]} & \multirow{3}{*}{2018} & Bat Meta-Heuristic Algorithm, Optimum & \multirow{3}{*}{Bit Meta-Heuristic Algorithm, Optimum } & \multirow{3}{*}{MSRNet, } \\ \cline{3-3}  & & Thresholding, Grayscale Conversion, & & \\ \cline{3-3}  & Morphological Operations, Ellipse Fitting & & & \\ \hline \multirow{3}{*}{[88]} & \multirow{3}{*}{2018} & Adaptive Threshold, Local Contrast & \multirow{3}{*}{Adaptive Threshold, } & \multirow{3}{*}{ACC = 99\%, ACC = 97\%} \\ \cline{3-3}  & Enhancement, Mathematical Morphology, & Mathematical & & \\ \cline{3-3}  & & Graycale Conversion, Gaussian Smoothing, Histogram & & \\ \cline{3-3}  & & & & \\ \cline{3-3}  & & &