# Classification and Segmentation of Diabetic Retinopathy: A Systemic Review
## 4 Segmentation  
Segmentation is a crucial process used on fundus images because it helps to identify an area of interest that is frequently difficult to diagnose and greatly aids in DR detection. The retinal images are divided into several pixel groups or regions that each represent a different anatomical feature, such as fovea, OD, micro-blood vessels, and multiclass DR lesions including HMs, MAs, SE, and HE [61; 62]. This allows the ophthalmologist to perform an eye-screening examination designed for the early detection of DR [63]. An ophthalmologist can manually identify DR by looking at the retinal fundus images and analyzing the morphological and macula changes in retinal blood vessels, HMs, HE, and MAs. This is a challenging, expensive, and time taking task. This task can be easily carried out by an automated system using artificial intelligence technology, particularly when testing for early DR [19]. Computerized techniques based on DL and other methods have aided early DR detection. Figure 6 shows the segmentation process of DR, in which images is taken from IDRiD dataset. Retina blood vessels and OD segmentation is done by the U-Net model. The approach is employed on open-access datasets and obtains an accuracy of 96.60% on EyePACS-1, 93.90% on Messidor-2, and 92.20% on DIARETDB0 [64]. To segment, the OD and retina blood vessels the morphological operation and 2D discrete wavelet are used. The methodologies are evaluated on the DIARETDB1 dataset and obtain 87.56% of specificity [65]. The U-Net model depends on CNN utilized to segment the HMs. The proposed experiment obtained an accuracy of 98% [66]. For OD segmentation watershed transform and adaptive active contour is used. These methodologies are evaluated on the IDRiD dataset and obtain 60% accuracy [67]. The MSRNet model is proposed for the segmentation of MAs. The model is evaluated on the e_ophtha_MAs dataset and obtains a sensitivity of 71.50% [68]. Figure 6: Segmentation process for DR [10]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref \#** & **Year** & **Methodology** & **Preprocessing Methods** & **Datasets** & **Results** \\ \hline \multirow{3}{*}{[56]} & \multirow{3}{*}{2020} & EfficientNet-B5, Batch Normalization, & \multirow{3}{*}{Local Average Color Filter, \(\,\)} & \multirow{3}{*}{APTOS} & \multirow{3}{*}{ACC = 90\%} \\  & & Rectified Adam Optimizer, Group & & Gaussian Blur & \\  & & Normalization, Gaussian Blur & Mask, CLAHE & & \\ \hline \multirow{3}{*}{[60]} & \multirow{3}{*}{2020} & Grayscale Conversion, Morphological & \multirow{3}{*}{Top-Hat Transform, \(\,\)} & \multirow{3}{*}{Grayscale Conversion, \(\,\)} & \multirow{3}{*}{DIARETDB0, \(\,\)} \\  & & Operations, Regional Minima (RMIN) & & & \\ \cline{1-1} \cline{5-5}  & & Operator, CLAHE, Marker-Controlled & & Morphological Operations, & \\ \cline{1-1} \cline{5-5}  & & Watershed Segmentation, Morphological & \multirow{3}{*}{CLAHE, Morphological (MG), Top-Hat Transform} & & \\ \cline{1-1} \cline{5-5}  & & Gradient (MG), Top-Hat Transform & & Gradient (MG) & \\ \hline \multirow{3}{*}{[57]} & \multirow{3}{*}{2020} & Non-Local Mean Filter (NLFM), CLAHE, 2D & Non-Local Mean Filter & e-Ophtha, & \multirow{3}{*}{ACC = 96.95\%, \(\,\)} \\  & & Gaussian Low-Pass Filter, Top-Hat & (NLFM), CLAHE, 2D & DIARETDB0, & \multirow{3}{*}{ACC = 97.95\%, \(\,\)} \\  & & Transform, Green Channel & Gaussian Low-Pass Filter & DIARETDB1 & \multirow{3}{*}{ACC = 97.35\%} \\ \hline \multirow{3}{*}{[58]} & \multirow{3}{*}{2019} & Local Average Filter, Clipping, Fractional, & \multirow{3}{*}{Local Average Filter, Clipping} & \multirow{3}{*}{Kaggle} & \multirow{3}{*}{ACC = 86.17\%} \\  & & SVM, TLBO, Max-Pooling & & & \\ \hline \multirow{3}{*}{[59]} & \multirow{3}{*}{2018} & Image Resize, Wavelet Transform, Maxpool & \multirow{3}{*}{Image Resize} & \multirow{3}{*}{IDRID} & \multirow{3}{*}{ACC = 98.60\%} \\  & & Operation, Batch Normalization, Drop Out, & & & \\ \cline{1-1} \cline{5-5}  & & Adam Optimizer & & & \\ \hline \hline \end{tabular}
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