## 6 Conclusions  
In this study, the development and deployment of a deep learning-based diagnosis system to assist novel coronavirus pneumonia screening using CT imaging is proposed. The segmentation subsystem, developed using the popular U-Net architecture as the main framework, will highlight the position of the infected areas, whereas the classification subsystem and the semi-automated 3D reconstruction subsystem will give an idea about the probability of being infected with the virus, along with the infection rate in case of positive findings. The quantitative and qualitative evaluation results demonstrated the effectiveness of our system in accurately localizing and quantifying the infection regions from CT scans. It also showed that the developed models outperform the other recently proposed approaches which were evaluated using standard benchmark performance metrics such as the dice score, for which we achieved 0.98 and 0.91 for the lung and infection segmentation tasks, respectively, and the accuracy, for which we achieved 0.98 for the classification task. The main limitation of this study is the use of a small, but sufficient number, of model training data. Confidentiality restrictions and the high cost of labeling partly explain the absence of a large number of COVID-19 clinical CT images. Indeed, combining datasets collected under different labeling regimes is often problematic because, in general, the data collected is heavily influenced by the instructions provided to annotators. For this reason, a workflow aimed at obtaining good performances for the different systems' components, while at the same time, dealing with reduced datasets used for training, is proposed. For future work, we intend to fully automate the volume reconstruction module to enable a real time 3D screening result.  
Conceptualization, R.M. ([PERSON]); methodology, [PERSON] ([PERSON]) and N.B.; software, R.M. ([PERSON]); formal analysis, M.A.M. and B.G.-Z.; writing--original draft preparation, [PERSON] ([PERSON]) and N.B.; writing--review and editing, M.H.B. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Works** & **Lungs Segmentation** & **Infection Segmentation** \\ \hline [PERSON] et al. [38] & 0.977 & 0.673 \\ \hline [PERSON] et al. [44] & 0.956 & 0.761 \\ \hline [PERSON] [45] & 0.961 & 0.780 \\ \hline [PERSON] et [PERSON] [46] & 0.96 & 0.81 \\ \hline
**Proposed method** & **0.98** & **0.91** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Quantitative comparison of COVID-19 CT lung and infection segmentation results in terms of Dice Coefficient.  
**Funding:** This work was supported by The Francophone University Agency (AUF) COVID-19.1 PANDEMIC SPECIAL PLAN. Project ID 469, Title: Development and implementation of a decision support system based on artificial intelligence applied to medical imaging to improve the management of patients with SARS-CoV2 (SPECTRUM) (2020).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the lung CTS dataset of [PERSON] et al. Further details can be found at: [[https://coronacases.org/](https://coronacases.org/)]([https://coronacases.org/](https://coronacases.org/)) (accessed on 15 December 2021) and [[https://radiopaedia.org/](https://radiopaedia.org/)]([https://radiopaedia.org/](https://radiopaedia.org/)) (accessed on 15 December 2021).  
**Conflicts of Interest:** The authors declare no conflict of interest.