# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 4 Discussion
### Diabetic Retinopathy Segmentation  
DR segmentation-based studies include those studies that segment retinal blood vessels, OD, or ROI from fundus images. Tables 4 and 5 provides a comparison of the reviewed DR retinal blood vessel segmentation studies grouped according to the datasets used by them. The segmented results obtained will be helpful for extended research in the area of DR. According to these tables, it is obvious that most of the studies obtained good accuracies but the specificity and sensitivity values are unavailable for some studies. It is \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Method** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline \multirow{8}{*}{STARE} & [PERSON] et al. [74] & **0.997** & 0.806 & **0.997** \\ \cline{2-4}  & [PERSON] et al. [67] & 0.965 & 0.81 & 0.984 \\ \cline{2-4}  & [PERSON] [70] & 0.986 & 0.812 & - \\ \cline{2-4}  & [PERSON] et al. [66] & 0.95 & 0.938 & 0.99 \\ \cline{2-4}  & [PERSON] et al. [76] & 0.972 & 0.864 & 0.98 \\ \cline{2-4}  & [PERSON] et al. [75] & 0.991 & 0.707 & 0.996 \\ \cline{2-4}  & [PERSON] and [PERSON] [77] & 0.965 & 0.898 & 0.97 \\ \cline{2-4}  & [PERSON] et al. [78] & 0.964 & - & - \\ \cline{2-4}  & [PERSON] et al. [79] & 0.967 & **0.986** & 0.796 \\ \cline{2-4}  & [PERSON] et al. [80] & 0.964 & 0.774 & 0.986 \\ \cline{2-4}  & [PERSON] et al. [81] & 0.966 & 0.841 & 0.981 \\ \cline{2-4}  & [PERSON] et al. [86] & 0.949 & 0.844 & 0.964 \\ \cline{2-4}  & [PERSON] et al. [87] & 0.915 & 0.818 & 0.942 \\ \cline{2-4}  & [PERSON] et al. [72] & 0.967 & 0.8 & 0.986 \\ \cline{2-4}  & [PERSON] and [PERSON] [73] & 0.945 & 0.802 & 0.956 \\ \cline{2-4}  & [PERSON] and [PERSON] [88] & 0.973 & 0.827 & 0.985 \\ \hline \multirow{8}{*}{DRIVE} & [PERSON] et al. [74] & 0.99 & 0.803 & **0.998** \\ \cline{2-4}  & [PERSON] et al. [67] & 0.972 & 0.821 & 0.988 \\ \cline{2-4}  & [PERSON] [70] & 0.977 & 0.821 & - \\ \cline{2-4}  & [PERSON] et al. [92] & **0.999** & 0.962 & - \\ \cline{2-4}  & [PERSON] et al. [69] & 0.933 & - & - \\ \cline{2-4}  & [PERSON] et al. [76] & 0.968 & 0.827 & 0.982 \\ \cline{2-4}  & [PERSON] et al. [75] & 0.99 & 0.701 & 0.995 \\ \cline{2-4}  & [PERSON] and [PERSON] [77] & 0.961 & 0.828 & 0.974 \\ \cline{2-4}  & [PERSON] et al. [78] & 0.957 & - & - \\ \cline{2-4}  & [PERSON] et al. [79] & 0.958 & 0.799 & 0.981 \\ \cline{2-4}  & [PERSON] et al. [80] & 0.954 & 0.763 & 0.982 \\ \cline{2-4}  & [PERSON] et al. [81] & 0.956 & 0.834 & 0.973 \\ \cline{2-4}  & [PERSON] et al. [83] & 0.977 & **0.967** & 0.996 \\ \cline{2-4}  & [PERSON] [84] & 0.958 & 0.814 & 0.906 \\ \cline{2-4}  & [PERSON] et al. [71] & 0.989 & 0.941 & 0.988 \\ \cline{2-4}  & [PERSON] and [PERSON] [85] & 0.906 & 0.918 & 0.905 \\ \cline{2-4}  & [PERSON] et al. [86] & 0.974 & 0.894 & 0.988 \\ \cline{2-4}  & [PERSON] et al. [87] & 0.921 & 0.834 & 0.961 \\ \cline{2-4}  & [PERSON] et al. [72] & 0.957 & 0.785 & 0.982 \\ \cline{2-4}  & [PERSON] and [PERSON] [73] & 0.958 & 0.792 & 0.971 \\ \cline{2-4}  & [PERSON] and [PERSON] [88] & 0.968 & 0.836 & 0.981 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of deep-learning based diabetic retinopathy segmentation.The **bold** and underlined fonts correspondingly show the **first** and **second** place. clear that on the STARE dataset, the authors of [74] attained the highest accuracy value of 99.7% which is better by 0.6% than the second highest accuracy value of 99.1%, obtained by the authors in [75] and 1.1% better than the accuracy values obtained by other methods. They could also achieve the highest specificity value compared to other methods. Whereas, when sensitivity is taken into account, the highest value was obtained by the authors in [66]. When we look at the values obtained for the DRIVE dataset in the table, the same study that achieved the highest accuracy value for the STARE dataset also obtained the best value for specificity as well as the second-best value for accuracy.