# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 2 Related Works
### Diabetic Retinopathy Lesion Detection
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Type** & **Method** & **Year** & **Technique** & **Dataset** \\ \hline \multirow{7}{*}{Exudates} & [PERSON] [17] & 2018 & Adaptive histogram equalization, CNN & DIARETDB0, DIARETDB1 \\ \cline{2-5}  & [PERSON] and [PERSON] [16] & 2019 & CNN and fuzzy c-means clustering, SVM & DRIVE, STARE \\ \cline{2-5}  & [PERSON] et al. [20] & 2019 & CNN & Self-collected \\ \cline{2-5}  & [PERSON] et al. [18] & 2020 & Deep CNN,multi-feature joint representation & E-Optha, DIARETDB1 \\ \cline{2-5}  & [PERSON] et al. [24] & 2020 & Inception-v3, ResNet-50 and VGG-19 & DIARETDB1, E-ophtha \\ \cline{2-5}  & [PERSON] and [PERSON] [12] & 2021 & rSVM, CNN & STARE, DIARETDB0, DIARETDB1 \\ \cline{2-5}  & [PERSON] et al. [19] & 2021 & SVM, RCNN, ResNet-50 & E-ophtha, DIARETDB1, MESSIDOR \\ \cline{2-5}  & [PERSON] et al. [22] & 2021 & Patch-based deep CNN & DIARETDB1, E-ophtha, IDRiD \\ \cline{2-5}  & [PERSON] et al. [23] & 2021 & RCNN & DIARETDB1, E-ophtha \\ \cline{2-5}  & [PERSON] et al. [25] & 2021 & SqueezeNet, ResNet50 and GoogLeNet & - \\ \cline{2-5}  & [PERSON] et al. [21] & 2022 & RNN & MESSIDOR \\ \cline{2-5}  & [PERSON] et al. [26] & 2022 & RTC-net & DIARETDB1, E-ophtha \\ \hline \multirow{7}{*}{Microaneurysm} & [PERSON] et al. [28] & 2020 & Deep CNN & IDRiD \\ \cline{2-5}  & [PERSON] et al. [30] & 2021 & MResUNet & DIARETDB1, IDRiD \\ \cline{2-5}  & [PERSON] et al. [27] & 2021 & MSRNet, MS-EfficientNet & E-Ophtha, IDRiD, DRIVE, CHASE DB1 \\ \cline{2-5}  & [PERSON] et al. [31] & 2022 & VGG-16, Inception V3, and ResNet50 & E-Ophtha, DIARETDB1 \\ \cline{2-5}  & [PERSON] et al. [29] & 2022 & VGG, Inception V3 & ROC \\ \hline \multirow{7}{*}{Microaneurysm} & [PERSON] et al. [32] & 2019 & cGAN, HEDNet & IDRiD \\ \cline{2-5}  & [PERSON] et al. [43] & 2019 & U-Net, SegNet & IDRiD, MESSIDOR \\ \cline{2-5}  & [PERSON] and [PERSON] [33] & 2019 & Super-pixel based segmentation & DIARETDB1, DRiD, HRF \\ \cline{2-5}  & [PERSON] et al. [39] & 2019 & CNN & IDRiD, E-Ophtha, MESSIDOR \\ \cline{2-5}  & [PERSON] et al. [48] & 2019 & VGG-19 & IDRiD \\ \cline{2-5}  & [PERSON] et al. [51] & 2020 & RCNN & DIARETDB1, MESSIDOR \\ \cline{2-5}  & [PERSON] et al. [40] & 2020 & Modified U-Net architecture & IDRiD, E-Ophtha \\ \cline{2-5}  & [PERSON] et al. [41] & 2020 & U-Net, Incremental learning & - \\ \cline{2-5}  & [PERSON] et al. [34] & 2021 & ResNet, Mask-RCNN & Self-collected \\ \cline{2-5}  & [PERSON] et al. [36] & 2021 & YOLOv3, CNN512 & DDR \\ \cline{2-5}  & [PERSON] et al. [37] & 2021 & AMWNet & IDRiD \\ \cline{2-5}  & [PERSON] et al. [44] & 2021 & U-Net, SVM & DRIVE, STARE, CHASEDB1, E-ophtha \\ \cline{2-5}  & [PERSON] et al. [49] & 2021 & SS-CNN, MS-CNN & DIARETDB1 \\ \cline{2-5}  & [PERSON] and [PERSON] [52] & 2021 & Mult-Attn-U-Net & IDRiD \\ \cline{2-5}  & [PERSON] et al. [38] & 2021 & YOLOv4 with darknet & DDR \\ \cline{2-5}  & [PERSON] et al. [35] & 2022 & JSeg model & IDRiD \\ \hline [PERSON] et al. [45] & 2022 & U-Net++, ResNet-18 & MESSIDOR, DIARETDB1 \\ \hline [PERSON] et al. [47] & 2022 & R-CNN & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Deep Learning based approaches for diabetic retinopathy lesion detection.