# Classification and Segmentation of Diabetic Retinopathy: A Systemic Review
## 4 Segmentation

Table 3: _Cont._The EAD-Net architecture based on CNN has proposed to segment the multiclass DR lesions like MAs, HMs, HE, and SE. The experiment is tested on e_ophtha_EX, IDRiD, and local datasets [69]. The fusing U-Net was employed for OD segmentation [70]. The approach uses thresholding, edge-based and region-based segmentation to segment retina blood vessels [71]. An approach based on GA and FCM is presented for the segmentation of DR lesions. The model is tested on 224 retinal images with a sensitivity of 78% [72]. Mathematical morphology operations are utilized to extract blood vessels for the segmentation of OD by using watershed transform. The experiment is tested on 130 fundus images that are taken from DIARETDB0 and DIARETDB1 dataset and perform well with a sensitivity of 87% and specificity of 93% [60]. The sliding band filter with adaptive threshold and region-growing approach is utilized for MAs segmentation. The experiment is performed on e_ophtha_MAs and SCREEN-DR datasets with sensitivities of 64% and 81% respectively [73]. A residual based U-Net model that used ResNet34 pre-trained model as the encoder is presented to segment the DR lesions like MAs and HE. The network was tested on e_ophtha_EX and IDRiD datasets and obtain 99.88% accuracy [74]. To segment the HE, we use k-means clustering based on automatic region-growing segmentation. The experiment is done on Messidor-2 and RF datasets and obtain 98.83% of accuracy [75]. The Otsu thresholding and region-growing technique are employed for OD segmentation. The proposed method improves model accuracy by 99% [76]. The modified deep convolution neural networks (DCNNs) model based on Segnet is employed on IDRiD dataset [77]. For the segmentation of DR lesions, the local-global U- Nets model is used. The model has been tested on the ISBI 2018 dataset [78]. The CNN based residual network is designed for exudates segmentation. The model performs well on e_ophtha and DIARETDB1 datasets. The model achieves 98% accuracy [79; 80]. To segment DR lesions such HMs, MAs, HE, and SE, the semantic segmentation model HEDNet is used. The proposed segmented model achieved a precision rate of 84.05% [81]. The deep CNN model that contains an encoder-decoder is proposed for the segmentation of OD, MAs, HMs, HE, and SE. The model is tested on Drishti-GS and IDRiD datasets and obtained with a Jaccard Index (IOU) of 85.72% [82]. The U-Net architecture is designed to segment the OD region. This model was employed on freely accessible datasets and achieved 95.80% of the dice coefficient [83]. DL model have been developed for MAs and exudates segmentation by using image patches. For experiment and performance analysis e-Ophtha is used as a dataset and achieved 95% of accuracy [84]. The methodology is proposed by using dynamic decision thresholding techniques for segment the exudates. The proposed segmented approach improve the model accuracy with 93.46% [85]. The bat algorithm [86] and threshold method is utilized for OD segmentation [87]. Adaptive-threshold and mathematical morphological operations have been utilized for exudates segmentation. The experiment is done on Messidor, DIARETDB1, E-Ophtha, and local datasets and gives a higher accuracy of 100% [88]. Circular Hough transform operation with morphological operations is utilized for the segmentation of OD Edge-based and morphological approaches are utilized for OD segmentation [89]. Region-growing techniques is proposed to segment the light and dark lesions of DR and assess the effectiveness of model. The methodology was evaluated on a local dataset and yielded 95% accuracy [90]. Table 4 summarizes the segmentation techniques.