# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 3 Datasets Used for Diabetic Retinopathy Segmentation/Lesion Detection  
The datasets being employed have a significant role in the accomplishment of the DL studies reviewed here. Some factors like the quality of these datasets, as well as the precision of their annotations, impact the results of such studies. Therefore, it will be useful to have a list of a few generally utilized fundus image datasets for DR segmentation/lesion detection. We have presented such a list in Table 3.  
Some of the generally utilized, publicly accessible datasets in such studies include DRIVE, STARE, IDRiD, E-ophtha, MESSIDOR, DIARET DB1, and DIARET DB0. Images gathered during a DR assessment program in the Netherlands were employed to create the dataset known as Digital Retinal Images for Vessel Extraction (DRIVE). It consists of twenty images each for training and testing sets [96]. The STARE dataset was collected as part of a project known as STARE (Structured Analysis of the Retina). It provides forty hand-labeled images for blood vessel segmentation [97]. The Indian DR Image Dataset (IDRiD) provides groundtruth for DR and Diabetic Macular Edema (DME). It contains annotations for DR grading, DR lesions, and optic disc [98]. The E-Optha dataset comprises of two sub-datasets called E-Optha-MA and E-Optha-EX [99]. Just as their names indicate these datasets have been annotated for EX and MA correspondingly. Another dataset known as DIARETDB1 \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Study** & **Year** & **Method** & **Dataset** \\ \hline [PERSON] et al. [68] & 2018 & U-Net, InceptionV1 & MESSIDOR \\ \hline [PERSON] et al. [69] & 2018 & U-Net segmentation & DRIVE \\ \hline [PERSON] et al. [80] & 2018 & CNN & STARE, DRIVE, CHASE-DB1 \\ \hline [PERSON] et al. [74] & 2019 & TPCNN, DLBSVM & STARE, DRIVE, HRF, REVIEW, DRIONS \\ \hline [PERSON] et al. [82] & 2019 & U-Net & - \\ \hline [PERSON] et al. [75] & 2019 & TPCNN, DLBSVM & STARE, DRIVE, HRF, REVIEW, DRIONS \\ \hline [PERSON] and [PERSON] [77] & 2019 & DNN & STARE, DRIVE, HRF \\ \hline [PERSON] et al. [78] & 2019 & DUNet & STARE, DRIVE, HRF CHASE-DB1 \\ \hline [PERSON] et al. [91] & 2020 & U-Net architecture & Self-collected \\ \hline [PERSON] et al. [90] & 2020 & RBFNN & DIARET DB0,DIARET DB1 \\ \hline [PERSON] et al. [93] & 2020 & Deep CNN model-SDL & MESSIDOR \\ \hline [PERSON] et al. [79] & 2020 & \(NFN+\) & STARE, DRIVE, CHASE-DB1 \\ \hline [PERSON] et al. [81] & 2020 & MSCNN-AM & STARE, DRIVE, CHASE-DB1 \\ \hline [PERSON] et al. [92] & 2020 & DRNet & IDRiD, HRF, DRIVE \\ \hline [PERSON] [84] & 2021 & Back propagation neural network & MESSIDOR, DRIVE \\ \hline [PERSON] et al. [95] & 2021 & ADL-CNN & EyePACS \\ \hline [PERSON] et al. [66] & 2021 & VGG-16 net architecture & DRIVE, STARE \\ \hline [PERSON] et al. [67] & 2021 & Multi-encoder decoder architecture & STARE,DRIVE \\ \hline [PERSON] et al. [87] & 2021 & Pix2 Pix GAN & CHASE DB1, DRIVE,STARE \\ \hline [PERSON] and [PERSON] [73] & 2021 & Encoder enhanced atrous U-Net & CHASE DB1, DRIVE, STARE, HRF \\ \hline [PERSON] and [PERSON] [88] & 2022 & D-MNet, pulse-coupled neural network & CHASE DB1, DRIVE, STARE, HRF \\ \hline [PERSON] [70] & 2022 & U-Net architecture & CHASE DB1, DRIVE,STARE \\ \hline [PERSON] et al. [71] & 2022 & U-Net++ architecture & DRIVE, MESSIDOR \\ \hline [PERSON] et al. [72] & 2022 & Context-involved U-Net & DRIVE, CHASE-DB1, STARE, HRF \\ \hline [PERSON] et al. [76] & 2022 & PLRS-Net, PLS-Net & DRIVE, CHASE-DB1, STARE \\ \hline [PERSON] et al. [89] & 2022 & Encoder-decoder with VGG-16 & DRIVE, DIARETDB0, CHASE-DB1, STARE \\ \hline [PERSON] et al. [83] & 2022 & SegNet, U-Net, CNN & DRIVE, CHASE-DB1, HRF \\ \hline [PERSON] and [PERSON] [85] & 2022 & AlexNet, U-Net & DRIVE, ARIA, MESSIDOR \\ \hline [PERSON] et al. [86] & 2022 & MSR-Net, GAN & DRIVE, ARIA, CHASE-DB1, STARE, HRF \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Deep-learning based approaches for diabetic retinopathy segmentation. has been annotated by four medical experts for DR lesions like MA and EX [100].