# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 2 Related Works
### Diabetic Retinopathy Retinal Blood Vessel/Optic Disc Segmentation  
This category includes those studies which performed retinal blood vessel segmentation as well as those which performed OD/region of interest (ROI) segmentation. Most studies fall under the first type, which helps to study the vascular tree and can thus help assess the current medical condition of the patient's eyes. Other studies focused on optic disc (OD)/fovea segmentation and ROI segmentation. These studies can aid the feature extraction process required for developing automatic DR diagnosis systems by helping to remove unnecessary features before doing them [60]. One of the most preferred DL methods for segmentation is to use CNNs [61; 62; 63; 64; 65]. The presence of neovascularization was used to spot proliferative DR by the authors in [66]. Different layers of CNNs were modeled together with VGG-16 net architecture for this. They performed blood vessel segmentation to remove the extra features before doing the classification. [PERSON] et al. [67] used a multi-encoder decoder architecture with two encoder units, as well as a decoder unit for retinal blood vessel segmentation. [PERSON] et al. [68] studied how the U-Net model performs for the same purpose. Dropout and batch normalization with various settings were utilized for this. Batch normalization was found to make U-Net learn faster but degraded the performance after epoch thirty. They also studied how the presence/absence of retinal blood vessel segmentation affected the DR classification performance using Inception v1. [PERSON] et al. [69] made use of U-Net for retinal segmentation with region merging. After this, CNN with ReLU activation function and max-pooling was utilized to perform DR grading. [PERSON] [70] applied a dual-tree discrete Ridgelet transform (DT-DRT) to fundus images for feature extraction from ROIs and used a U-Net-based method for retinal blood vessel segmentation after this. The U-Net++ architecture was used to extract the retinal vasculature by the authors in [71]. They used the extracted features to predict DR in the next stage of their experiment. A context-involved U-Net was used to perform retinal blood vessel segmentation in the study by the authors in [72]. They made use of patch-based loss weight mapping to improve the segmentation of thin blood vessels. Another study by the authors in [73] used an encoder enhanced atrous U-Net to extract the retinal vasculature from fundus images. They introduced two additional layers to the encoder at every stage to enhance the extraction of edge information. [PERSON] et al. [74] utilized contrast limited adaptive histogram equalization (CLAHE) to perform pre-processing. After this, feature generation and classification were performed. The former step was accomplished using the tandem pulse coupled neural network model and the latter was achieved using DL based support vector machine (DLBSVM). DLBSVM also extracted the blood vessel tree from the fundus images. Similar work was done by the authors of [75]. Another study by the authors of [76] made use of a pool-less residual segmentation network (PLRS-Net) for retinal blood vessel segmentation, which could segment smaller vessels better. They used two pool-less approaches called PLRS-Net and PLS-Net (pool-less segmentation network)