## 5 Discussion and Conclusions  
Through this research, we understood the vital role of bioinformatic applications in modern times. Fast, automatic and accurate vessel segmentation for diagnosis can even save lives. We approached the challenge of segmenting retinal blood vessels by treating it as a classification task. Since our work involves image processing, we chose the model of automatic encoders. For the construction of our auto-encoder, we chose convolutional layers. With its gradual construction, we understood what is more efficient for the model due to the variety of morphological structures eye vessels can have. The final convolutional auto-encoder, therefore, is trained on two datasets in a concise amount of time (35 min), having a competitive performance compared to other models that have been proposed in the past. The specificity metric has the highest value compared to all other models in both databases. This metric calculates the percentage of true negatives, and to be more understandable, it expresses how many pixels were correctly predicted as black, i.e., as non-vessels. The high specificity value can also be perceived practically through the images produced in the testing process. More specifically, as we have discussed in the comparison section, the corresponding images produced by our model are accurate, sharp and \"cleaner\" in the lines of the vessels and without excess noise.  
Our research and the model we introduced could be applied in real situations since the proposed convolutional auto-encoder is efficient enough compared to other models. In particular, it would be possible to construct a system that would have as input the automatically segmented images of the retinal blood vessels and as an output the information regarding the patient and if they are suffering from a disease or not.  
There are some directions that future work could examine. First, a more extensive scale evaluation could be designed, and additional datasets such as the High-resolution fundus and the CHASE-DB1 image databases to get an even better insight into the performance of our proposed method. Moreover, a deeper investigation-study of the layers could be the key to increasing the performance. Adding noise, such as Gaussian, could be examined to improve the model since it could help it better distinguish the vessels from the background. Also, another future work direction is the examination of techniques for creating the feature maps such as spatial pyramid networks. The examination of this direction concerns an essential aspect of our future work. Finally, the formulation of a web application with an interface to facilitate ophthalmologists in using our method in real-time easily constitutes another direction for future work.  
\begin{table}
\begin{tabular}{l c} \hline Operating System & Ubuntu 16.04.7 LTS \\ \hline CPU & Intel (R) Core (TM) i7-5960X 3.00 GHz \\ \hline RAM & 62 GB DDR4 \\ \hline GPUs & GPU0: G-FORCE GTX 1080 8 GB \\ \hline GPU1: G-FORCE GTX 1080 8 GB \\ \hline \end{tabular}
\end{table}
Table 3: Hardware Characteristics.  
**Author Contributions:** Conceptualization, I.P., P.T. and N.N.; methodology, I.P. and N.N.; software, N.N., I.P. and I.D.; validation, N.N. and I.P.; writing--original draft preparation, N.N., I.P. and I.H.; visualization, N.N. and I.D.; Writing--review & editing, NN, I.P. and P.T. supervision, I.H., P.T. and C.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by the Institute of Data Science and Digital Technologies, Image and Signal Analysis Group, Faculty of Mathematics and Informatics, Vilnius University of Lithuania and the Computer Engineering and Informatics Department, University of Patras, Greece.  
The authors declare no conflict of interest.