# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 4 Discussion
### Diabetic Retinopathy Segmentation
Whereas, the highest values for accuracy and sensitivity, 99.9% and 96.7% were obtained by the authors in [83; 92], respectively. Figure 4 provides a further comparison of retinal blood vessel segmentation results using the DRIVE dataset by the researchers in [75; 76]. We can observe that the \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Method** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline \multirow{3}{*}{REVIEW} & [PERSON] et al. [74] & **0.999** & **0.809** & 0.988 \\ \cline{2-6}  & [PERSON] et al. [75] & 0.995 & 0.693 & **0.989** \\ \hline \multirow{7}{*}{HRF} & [PERSON] et al. [74] & **0.99** & 0.808 & **0.997** \\ \cline{2-6}  & [PERSON] et al. [75] & **0.99** & 0.826 & 0.994 \\ \cline{2-6}  & [PERSON] and [PERSON] [77] & 0.853 & 0.866 & 0.852 \\ \cline{2-6}  & [PERSON] et al. [78] & 0.965 & - & - \\ \cline{2-6}  & [PERSON] et al. [83] & 0.979 & **0.979** & 0.995 \\ \cline{2-6}  & [PERSON] et al. [86] & 0.977 & 0.889 & 0.985 \\ \cline{2-6}  & [PERSON] et al. [72] & 0.96 & 0.857 & 0.969 \\ \cline{2-6}  & [PERSON] and [PERSON] [73] & 0.924 & 0.659 & 0.98 \\ \cline{2-6}  & [PERSON] and [PERSON] [88] & 0.967 & 0.754 & 0.985 \\ \hline \multirow{3}{*}{DRIONS} & [PERSON] et al. [74] & **0.999** & **0.805** & **0.998** \\ \cline{2-6}  & [PERSON] et al. [75] & 0.993 & 0.795 & 0.997 \\ \hline \multirow{7}{*}{CHASE DB1} & [PERSON] [70] & 0.96 & 0.802 & - \\ \cline{2-6}  & [PERSON] et al. [76] & 0.973 & 0.83 & 0.984 \\ \cline{2-6}  & [PERSON] et al. [78] & 0.961 & - & - \\ \cline{2-6}  & [PERSON] et al. [79] & 0.969 & 0.8 & **0.988** \\ \cline{2-6}  & [PERSON] et al. [80] & 0.961 & 0.764 & 0.981 \\ \cline{2-6}  & [PERSON] et al. [81] & 0.964 & 0.813 & 0.982 \\ \cline{2-6}  & [PERSON] et al. [83] & 0.955 & 0.837 & 0.982 \\ \cline{2-6}  & [PERSON] et al. [86] & **0.987** & **0.934** & 0.984 \\ \cline{2-6}  & [PERSON] et al. [87] & 0.934 & 0.917 & 0.969 \\ \cline{2-6}  & [PERSON] et al. [72] & 0.967 & 0.813 & 0.984 \\ \cline{2-6}  & [PERSON] and [PERSON] [73] & 0.934 & 0.646 & 0.965 \\ \cline{2-6}  & [PERSON] and [PERSON] [88] & 0.971 & 0.854 & 0.979 \\ \hline \multirow{3}{*}{IDRID} & [PERSON] et al. [37] & 0.983 & 0.485 & **0.999** \\ \cline{2-6}  & [PERSON] et al. [92] & **0.997** & **0.899** & - \\ \hline \multirow{3}{*}{MESSIDOR} & [PERSON] et al. [68] & 0.84 & - & - \\ \cline{2-6}  & [PERSON] [84] & **0.962** & **0.852** & **0.911** \\ \cline{2-6}  & [PERSON] and [PERSON] [85] & 0.834 & 0.792 & 0.844 \\ \hline \multirow{3}{*}{ARIA} & [PERSON] et al. [86] & **0.963** & **0.718** & **0.984** \\ \cline{2-6}  & [PERSON] and [PERSON] [85] & 0.925 & 0.566 & 0.961 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of deep-learning based diabetic retinopathy segmentation. The **bold** and underlined fonts correspondingly show the **first** and **second** place. former study clearly segments the retinal blood vessels without any trace of OD and fovea, whereas in the second study parts of the OD and fovea are present in the obtained result. One of the major challenges which researchers find while performing retinal blood vessel segmentation, is the segmentation of tiny blood vessels. Hence, more studies are required that can effectively detect these similar to the study of [76]. It would also be beneficial to classify the detected blood vessels into arteries/veins as well as to determine the width of these blood vessels, which will provide valuable information that can be used to determine the present condition of the patient's eyes. When it comes to OD segmentation, the major challenge is that other DR lesions may be wrongly segmented as OD due to their similar shape, size, and color.