# A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph
## 5 Conclusions and Future Work  
Panoramic radiographs can be a very effective tool to support patients' diagnosis and to define a treatment plan to them. The use of segmentation models to detect teeth and their exact limits can be of paramount importance for eliminating a task that is quite susceptible to human failure. In this work, we analyzed four models that are being used in the literature for other purposes in order to investigate their performance for classifying a panoramic radiograph database. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Dice (\%)**} & **Accuracy** & **Precision** & \multirow{2}{*}{**Recall (\%)**} & \multirow{2}{*}{**Parameter**} \\  & & **(\%)** & & & \\ \hline TSASNET [15] & 92.72 & **96.94** & 94.97 & 93.77 & 78,270,000 \\ U-NET (Ensemble) [18] & **93.58** & 95.17 & 93.69 & **93.91** & - \\ DoubleU-NET w/data augmentation & 92.886 & 96.591 & 93.095 & 92.705 & 29,264,930 \\ Nano-Net w/data augmentation & 89.855 & 95.576 & **96.326** & 84.222 & **235,425** \\ DoubleU-NET w/data augmentation & 92.695 & 96.552 & 94.184 & 91.283 & 29,264,930 \\ Nano-Net w/data augmentation & 91.739 & 96.173 & 93.783 & 89.815 & **235,425** \\ \hline \hline \end{tabular}
**Bold cells** show the best result for that metric.  
\end{table} 
Table 5: Comparison with literature models. The results obtained in this work are satisfactory and present paths for a better and more effective dental segmentation process. All four models tested had good results. Originally, these models were proposed with another purpose and when adapting them for dental segmentation in panoramic radiographs, they presented good performance in the face of the evaluated metrics. However, two models deserve to be highlighted. DoubleU-Net presented a very competitive performance, with results very close and even better than those in the literature while using a high number of parameters trained, going from 78 M (TSASNet) to 29 M (DoubleU-Net). On the other hand, the Nano-Net used only 235k trained parameters (which represents less than 1% of the TSANet), and was able to obtain promising results in terms of accuracy, precision, recall and Dice, thus showing that it is a model that can be explored for better results. A limitation of this work is in relation to the database used. As described in Section 3.1, all images were recorded by the same machine, so the deep learning models can have this performance only for the parameters used by that given machine. As the next steps of this research, it is planned to further evaluate the learning process of DoubleU-Net and Nano-Net to mold the creation of a new segmentation model that can better detail and evaluate dental panoramic radiograph images. Another way is to perform an analysis of the images generated as the output of each model in order to propose an improvement in ground truth. When analyzing the database used, it is noticed that some dental roots are square rather than more rounded. This can be a characteristic of human fatigue, since we are dealing with a dataset that is too large for the area. Another step of this research is to look for datasets that contain more variable data, obtained from different machines with different parameters for a greater generalization of the trained models. Conceptualization, E.d.S.R. and P.T.E. ; Data curation, E.d.S.R. and P.T.E. ; Formal analysis, E.d.S.R. and P.T.E. ; Funding acquisition, P.T.E. ; Investigation, E.d.S.R. and P.T.E. ; Methodology, E.d.S.R. and P.T.E. ; Resources, P.T.E. ; Software, E.d.S.R. ; Supervision, P.T.E. ; Validation, E.d.S.R. and P.T.E. ; Visualization, P.T.E. ; Writing--original draft, E.d.S.R. and P.T.E. ; Writing--review & editing, E.d.S.R. and P.T.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.