# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 4 Discussion
### Lesion Detection  
DR lesion detection-based studies include studies that detected DR lesions like MA, EX, and HM. This section attempts to compare these studies using three metrics which include accuracy, sensitivity, and specificity. The values for these are stated in many studies. Table 6 provides a comparison of DR lesion-detection-based studies. The studies have been grouped according to the type of lesions they detected as well as according to the datasets used by them. In the case of EX, the maximum number of studies used the DIARET DB1, and the highest value for accuracy was obtained by the authors in [24] which is 0.2% higher than the second-best accuracy value obtained by the authors in [44]. The highest sensitivity value was obtained by the authors in [17], whereas the authors in [26] obtained the highest specificity value. From the table, it can also be seen that the E-Optha dataset has been used by many studies for HE and MA detection. In the first case, the highest accuracy and specificity values of 98.4% and 98.8%, respectively, were achieved by the authors in [39]. They also achieved the best accuracy and specificity values of 99.2% and 99.8%, respectively, for MA detection also. Whereas, the highest values of sensitivity for HE and MA detection on the E-Optha dataset were achieved by the authors in [22; 53; 54]. In the case of HM, the maximum accuracy of 99.4% on the MESSIDOR dataset was obtained by the authors in [59]. Whereas, the maximum accuracy value of 97.2% on the DIARET DB1 dataset was obtained by the authors in [58]. Figure 5 provides a comparison of MA detection results on the IDRiD dataset by the researchers in [35; 43] and a comparison of HE detection results on the IDRiD dataset by the researchers in [35; 43]. As seen in the figure, the authors in [43] could effectively detect HE using U-Net and MA using a modified U-Net. Whereas, the authors in [35] could obtain similar results using the JSeg model. Figure 6 provide a further comparison of the results obtained by researchers for deep-learning-based HE and MA detection on the IDRiD dataset. It can be clearly seen that the authors in [PERSON] et al. have obtained the best value of 99.9% for all three metrics for both HE and MA detection on the IDRiD dataset. This is followed by the study by the authors in [PERSON] et al., which achieved very close values for accuracy and specificity. In the case of HE detection, all the studies could achieve accuracy values greater than 98% except for the study by the authors in [PERSON] et al. [48]. Just like DR segmentation, there are several challenges to performing DR lesion detection. If we take the case of MA detection, the main challenge is their very small size. But MA detection is very important to determine the onset of DR. EX detection is also challenging since they may not have well-defined boundaries. Additionally, as mentioned before, the OD and fovea may be mistakenly detected as lesions due to their similar shape, size, and color. Finally, the size of DR lesions, like EX, vary according to the severity of DR which makes it even more challenging.