## 4 Experimental Study
### Comparison against Existing Methods  
A deep and concrete comparative study has been performed to provide insight into the performance of our introduced method and compare its performance towards recent related works in the field. Comparing our autoencoder with other models (see Tables 1 and 2), we notice that our proposed autoencoder is trained in fewer epochs and performs quite well. Also, it has the highest specificity. It should be noted that the training process of other models lasts many hours since they are trained with 100-150 epochs. Specifically, the SA-UNet proposed by [PERSON] et al. [30] is the newest model with the best overall performance trained in 150 epochs. On the other hand, our proposed convolutional autoencoder is trained significantly faster (within 35 min), and its results are very competitive, with the specificity surpassing any other model's. Specificity essentially measures the percentage of correct black pixels in evaluation images. This can be seen from the final result, after which  
Figure 6: ROC curves of our model. On the left is the ROC curve for the DRIVE dataset and the ROC curve for the STARE on the right. the images are pretty \"clean\" without the noise we observe in other models. For example, an attempt at automatic segmentation of the blood vessels by the model of [PERSON] et al. is shown in Figure 8. On the left, we can observe that the octave convolutional neural network adds enough noise in the final attempt to segment the retina image. On the contrary, our convolutional autoencoder has a more precise result without noise. These photos belong to the STARE database. It is important to emphasise that in the DRIVE database, each image's masks are available, allowing us to calculate the performance metrics only in the FOV (field of view) and not in the pixels of the background. In the STARE database, the masks are unavailable; therefore, the metric evaluation results include all the images' pixels. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **AUC\_ROC** & **ACC** & **SPEC** & **F1 SCORE** & **PREC** & **SENS** \\ \hline [PERSON] et al. [14] & 96.52 & 90.64 & 92.83 & 90.66 & 92.47 & 89.90 \\ \hline [PERSON] et al. [18] & 98.02 & 95.66 & & & & \\ \hline [PERSON] et al. [21] & **98.68** & **97.06** & 98.36 & 83.24 & & 83.46 \\ \hline [PERSON] et al. [22] & 98.15 & 96.15 & 98.07 & & & 82.13 \\ \hline [PERSON] et al. [31] & 96.10 & 94.10 & 96.90 & & & 73.50 \\ \hline [PERSON] et al. [32] & 96.48 & 94.74 & 97.98 & & & 72.52 \\ \hline [PERSON] et al. [33] & 84.80 & 94.40 & 97.80 & & & 71.60 \\ \hline [PERSON] et al. [34] & & 95.23 & & & & 76.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of the introduced convolutional autoencoder on the DRIVE dataset. Figure 7: Segmentation results using the proposed model. (**a**) Results of automated segmentations by our model; (**b**) Corresponding manual segmentations. Therefore, our initial idea was to build an automatic encoder by specifying a structure suitable for producing reconstructed images without noise, and it was very efficient. So, we built a convolutional autoencoder, a unique stream of a neural network, for the blood vessel segmentation task. Through the evaluation of our model, we saw that it is competitive with other proposals using past models and has the best specificity value. Another advantage of our autoencoder is the short time the training process takes. More specifically, our model takes up to 35 min to learn to automate segment fundus images. Practically, our model differs in the final results since the images produced are much cleaner without noise and without the creation of vessels that do not exist. In Tables 1 and 2, performance comparisons of our model towards works in the literature are presented. Finally, it is worth indicating that we designed, formulated and evaluated our model in a machine with the characteristics in Table 3.