# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 2 Related Works
### Diabetic Retinopathy Retinal Blood Vessel/Optic Disc Segmentation
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Type** & **Method** & **Year** & **Technique** & **Dataset** \\ \hline \multirow{8}{*}{Multiple Lesions} & [PERSON] et al. [50] & 2022 & RTNet & IDRiD, DDR \\ \cline{2-5}  & [PERSON] and [PERSON] [53] & 2022 & DARNet & IDRiD, DDR, E-ophtha \\ \cline{2-5}  & [PERSON] and [PERSON] [54] & 2022 & CARNet & IDRiD, DDR, E-ophtha \\ \cline{2-5}  & [PERSON] et al. [46] & 2022 & DR-ResNet50 & DIARETDBI, E-ophtha, IDRiD \\ \cline{2-5}  & [PERSON] et al. [55] & 2022 & YOLOv5 & DDR \\ \cline{2-5}  & [PERSON] et al. [42] & 2022 & U-Net & MESSIDOR, DIARETDB1 \\ \cline{2-5}  & [PERSON] et al. [56] & 2023 & U-Net, CNN & APTOS, MESSIDOR \\ \hline \multirow{8}{*}{Hemorrhage} & [PERSON] et al. [59] & 2021 & 3D CNN with feature fusion & DIARETDB0, DIARETDB1, \\ \cline{2-5}  & [PERSON] et al. [57] & 2022 & U-Net & IDRiD, DIARETDB1 \\ \cline{1-1} \cline{2-5}  & [PERSON] et al. [58] & 2023 & HemNet & DIARETDB0, DIARETDB1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._which did not require any pre/post-processing. A multilevel deep neural network with a three-plane preprocessing method was used by the authors of [77] for retinal blood vessel segmentation. The first four convolutional layers of VGG-16 were fine-tuned for blood vessel feature extraction. [PERSON] et al. [78] performed retinal blood vessel segmentation using a new model known as deformable U-Net (DUNet). This model combined the benefits of the U-Net architecture with those of a deformable convolutional network. Another new network followed, a network called \(NFN+\) was used by the authors of [79] to segment retinal blood vessels. A distinct cascaded design, along with inter-network connections made this model unique. Distinctive segmentation of thick as well as thin retinal blood vessels was performed by the authors of [80] by using a three-stage DL model. These stages consisted of the segmentation of thick vessels, the segmentation of thin vessels, and the fusion of vessels. Another model known as multi-scale CNN with attention mechanisms (MSCNN-AM) was used by the authors in [81] to segment retinal blood vessels using various dimensions. Atrous separable convolutions which had differing dilation rates were used to seize global and multi-scale vessel data more effectively. [PERSON] et al. [82] used an enhanced U-Net architecture in order to segment retinal blood vessels. They made use of data from the West China Hospital of Sichuan University to perform their experiments. Three DL models consisting of SegNet, U-Net, and CNN were used by the authors of [83] for the same task. The SegNet model was found to be better than the other two models in segmenting both thin and thick blood vessels. A back propagation neural network was utilized by the researchers in [84] to extract the retinal vasculature from fundus images. They could obtain a reduction in operation time in addition to improved accuracy. A pre-processing AlexNet block followed by an encoder-decoder U-Net model was utilized by the authors in [85] to extract the retinal vasculature. Their study aimed to support the diagnosis of several ophthalmological diseases including DR. Another similar study that performed extraction of the retinal vasculature was performed by the authors in [86] and used a multi-scale residual CNN known as MSR-Net, along with a generative adversarial network (GAN). The generator of the GAN was a segmentation network, whereas the discriminator was a classification network. Another GAN called Pix2 Pix GAN was used by the authors in [87] for retinal blood vessel segmentation to help studies that detect different ophthalmological diseases. A novel model known as D-MNet with multi-scale attention and a residual mechanism, was used by the authors in [88] for retinal blood vessel segmentation. It was used along with an enhanced version of pulse-coupled neural network to achieve this task. Some studies performed OD segmentation. This was achieved by using an encoder-decoder with the VGG-16 backbone by the authors of [89].