# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 2 Related Works
### Diabetic Retinopathy Lesion Detection  
The studies that were devoted to the detection of DR lesions like MA, HM, and EX fall under this category. Another group of studies detected multiple lesions as well as OD. A set of proposed methods are discussed in this section as well as a summarization of them in terms of techniques and datasets used in Table 1 summarizes. Some studies reviewed, focused on detecting EX. [PERSON] and [PERSON] [16] used adaptive histogram equalization for preprocessing followed by blood vessel segmentation using CNN. After this, EX segmentation was performed using fuzzy c-means clustering. Finally, DR classification was achieved using SVM. [PERSON] [17], combined a circular Hough transform with CNNs to detect EX. The circular Hough transform helps to ignore OD. EX detection was performed after segmentation of EX. SVM based on the ranking was implemented along with CNN architecture to accurately segment fundus images by the authors in [12]. They also detected EX after segmentation. HE detection with deep CNN and multi-feature joint representation was performed by [PERSON] et al. [18] using a new methodology. They utilized combined features that included deep features as well as handcrafted features. HE detection using SVM blended with a speedier region-based CNN (RCNN) was performed by the authors of [19]. [PERSON] et al. [20] performed EX classification. They used a CNN to investigate if the classification performance improves if more importance was given to the image characteristic inside an EX. The outcomes showed that the performance improved. Recurrent neural network (RNN) based semantic features were used for EX segmentation by the authors of [21] for detecting DR. [PERSON] et al. [22] used a super-pixel segmentation algorithm, followed by multi-feature extraction and patch generation. Finally, CNN was used for HE detection. RCNN with MobileNet was utilized as a feature extractor by the authors of [23] for EX detection. [PERSON] et al. [24] used pre-trained Inception-v3, ResNet-50, and VGG-19 to perform feature extraction before EX classification using the softmax classifier. Another study by the authors of [25] used three different models including SqueezeNet, ResNet50, and GoogLeNet for EX detection. Among these three, ResNet50 was found to provide the best results. A CNN with residual skip connection was used for EX detection by the authors in [26]. The architecture used was called RTC-net. Few studies focused on MA detection. Multi-scale residual network (MSRNet) was used for segmentation by the authors of [27] to detect tiny MA. It was based on U-Net architecture. After this, classification was achieved using MS-EfficientNet (multi-scale efficient network). [PERSON] et al. [28] used the prognosis of MA for DR grading into three, which includes early NPDR, moderate NPDR, and severe NPDR. Deep CNN was used to perform semantic segmentation of fundus images. Gaussian answer Laplacian (LOG) and maximum matching filter response (MFR) filters were used for lesion identification. Feature extraction using VGG and Inception V3 backbones, alongside transfer learning was utilized for MA detection by the authors of [29]. After this, several machine learning classifiers were used for classifying input images. [PERSON] et al. [30] modified the U-Net model and used it for MA segmentation. Residual units with modified identity mapping were used in this new model known as MResUNet. [PERSON] et al. [31] made use of three different transfer learning-based CNN models for MA detection. These consisted of VGG-16, Inception V3, and ResNet50. They also performed the same task by making use of a random forest approach. Some studies did a general detection of DR lesions. Conditional generative adversarial network (cGAN) was used for pixel-level lesion segmentation by combining with holistically-nested edge detection (HEDNet) by the authors in [32], and it was found that adversarial loss advances the lesion segmentation performance.