# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 2 Related Works
### Diabetic Retinopathy Lesion Detection
An approach known as super-pixel-based segmentation was used by the authors of [33] to compare the performance of three different algorithms under a single unified framework in performing multi-lesion detection. Out of these, a compacted watershed algorithm was found to be faster, whereas a linear spectral clustering (LSC) algorithm generated better segmentation results. After this, a single-layer CNN was used to detect lesions. [PERSON] et al. [34] introduced a novel system called DeepDR comprising of three sub-networks (based on ResNet andMask-RCNN) to grade DR using a large dataset collected between 2014 and 2017. Lesion segmentation was performed before DR grading. The Javeria segmentation (JSeg) model, a novel model with the ResNet-50 model as the backbone, was utilized for segmentation by the authors in [35]. In their next phase, Resnet-101 was used for feature extraction built on the equilibrium optimizer (EO). After this, support vector machine (SVM) and neural network classifiers were used for grading lesions. The YOLOv3 model was used for lesion localization by the authors in [36]. It was fused with the CNN512 model to grade DR and localize DR lesions. [PERSON] et al. [37] introduced a new model called adaptive multi-target weighting network (AMWNet) to segment DR lesions. It consisted of an encoder, decoder and an adaptive multi-target weighting module. After this reverse data recovery network (RRN) was used to replicate the cycle perception as part of the hierarchy. DR lesion detection using a CNN-based one-shot detector was used by the authors in [38]. The YOLOv4 deep neural network (DNN) architecture was used along with the Darknet framework. Many studies reviewed, detected multiple DR lesions. Pixel-wise segmentation of MA, EX, and OD were achieved by the authors of [39] using a novel membrane system termed \"dynamic membrane system\" and mask R-CNN. MA and EX segmentation was performed by the authors of [40] by using a revised U-Net architecture which was grounded on the residual network. U-Net with incremental learning was used by the authors in [41] for EX and HM segmentation. A new incremental learning standard was introduced to excerpt the acquaintance gained by the preceding model to advance the present model. Another study that detected EX and HM was performed by the authors in [42]. They performed a semantic segmentation with three classes and used a color space transformation along with U-Net. [PERSON] et al. [43] used one individual network per one type of lesion related to DR (e.g., one network for MA, one network for HE, etc.). They found that this modification yielded better results for U-Net to classify MA, SE, and HM and detect DR. U-Net segmentation followed by classification using support vector machine was achieved by the authors of [44] for grading DR by detecting some DR lesions. Blood vessels, EX, MA, and HM were segmented for this purpose. A nested U-Net architecture known as U-Net++ was used for red lesion segmentation (MA and HM segmentation) from fundus images by the authors in [45]. This was followed by sub-image classification using ResNet-18 to reduce false positives. Another study that focused on red lesion detection was one by the authors of [46]. For this purpose, they fine-tuned various pre-trained CNN models with an augmented set of image patches and found ResNet50 to be the best. They modified it with reinforced skip connections to get a new model called DR-ResNet50. The authors in [47] used MA, EX, and HM diagnosis to detect DR by using regions with CNNs (R-CNN). Feature extraction using the VGG19 model trialed by classification using different machine learning classifiers by the authors of [48]. They identified and classified DR lesions into MA, SE, HE, and HM. A single-scale CNN (SS-CNN) followed by a multi-scale CNN (MS-CNN) was used by the authors of [49] to perform pixel-level detection of MA and HM. Four DR lesions, including MA, HE, SE, and HM, were segmented by the authors of [50] using a relation transformer network (RTNet).