# Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning
## 3 Experimental Results
### Comparative Studies  
In this section we compare our method for the DR, DME, and glaucoma detection against state-of-the-art methods. For DR detection we compared our method against the works of [PERSON] et al. [28], [PERSON] et al. [57], [PERSON] et al. [58], [PERSON] et al. [59], [PERSON] et al. [60], and [PERSON] et al. [61] using a 10-fold cross validation scheme. The proposed method was implemented using Matlab-2019 and run on an Nvidia GTX1070 GPU-based system. The comparative results using accuracy (Acc), specificity (SP), sensitivity (SE), and area under the curve (AUC) are reported in Table 7. From the results it can be observed that our method achieved 95% accuracy, which is highest then all the compared methods. In terms of specificity, the work of [PERSON] et al. [59] was the closest to our method where the difference is just 0.5%. However, this method exhibited the lower sensitivity, where our method supersedes this approach by a significant margin of 8%. Although, the method of [PERSON] et al. [58] showed higher sensitivity then our method, where performance difference is 3%; but contrary to this, the method showed approximately 10% more performance loss than our method in terms of specificity. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Database** & **Acc** & **Dc** & **SP** & **SE** \\ \hline
**HRF** & 0.958 & 0.952 & 0.97 & 0.957 \\
**DR HAGIS** & 0.943 & 0.89 & 0.961 & 0.955 \\
**ORIGA** & 0.957 & 0.943 & 0.954 & 0.943 \\
**Average** & 0.9526 & 0.9283 & 0.961 & 0.951 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of Proposed method over three databases. Figure 8: Segmentation Results (**a**). input images, (**b**). localized images, (**c**). OD segmentation, (**d**). OD ground truths, (**e**). OC segmentation, and (**f**). OC ground truths. Figure 9: False detection results. The higher sensitivity rate with lower specificity rate reflects that the method by [PERSON] et al. [58] mislabel the non-DR signs as DR, which may lead towards the imprecise diagnosis for the healthy patients. Similarly, in terms of AUC our method also outperformed all the comparative approaches. The consistent performance of the proposed method is attributed towards the low-resolution feature map generation based on the region proposals. The region proposals assist in accurate localization of the DR even for the images suffering from the low illumination. Hence, based on the results it can be concluded that our method is more precise for DR sign detection as compared to the other approaches used for the comparison. For DME detection we have compared our method against the works of [PERSON] et al. [62], [PERSON] et al. [63], [PERSON] et al. [64], [PERSON] et al. [2], [PERSON] et al. [65], [PERSON] et al. [66], [PERSON] et al. [67], [PERSON] et al. [68] and [PERSON] et al. [69] using the MESSIDOR dataset. The results of the comparison are presented in Table 8. In terms of sensitivity, our method has achieved the SE of 0.96 which is equal to [PERSON]'s method. However, in terms of specificity and accuracy, our method performed better than [PERSON]'s method and all other methods. Significant difference in terms of sensitivity can be observed against [PERSON] et al. [62], [PERSON] et al. [65], and [PERSON] et al. [66], where this difference ranges from 11-26%. The method [PERSON] et al. [65] which showed higher specificity in comparison to the sensitivity shows that the method considered the DME regions as non-DME regions, whereas, the methods, i.e., [PERSON] et al. [66] where sensitivity is higher than the specificity reflect that the method considered even the non-DME regions as DME. According to the Table 8, our method has achieved the SP of 0.958 which is higher than other methods (i.e., 0.55-0.95), although SP of [PERSON]'s method is 0.97 but its SE value is lower than our method. Hence, the performance comparison reflects that our method reliably detects the DME. For glaucoma detection, we compared our method against the works of [PERSON] et al. [70], [PERSON] et al. [71], [PERSON] et al.