## 3 Results
The proposed model outperformed in correctly identifying positive instances (Sensitivity), accurately identifying negative instances (Specificity), and achieving a balanced performance between precision and recall (F1-score). The confusion matrix and performance graphs suggest that the SellaMorph-Net model provides more accurate characterizations (\(\textbf{P}_{\textbf{S}}\) = 96.832, \(\textbf{Sn}_{\textbf{s}}\) = 98.214, \(\textbf{Sp}_{\textbf{s}}\) = 97.427, \(\textbf{F1}_{\textbf{s}}\) = 97.314) than the compared models VGG19 (\(\textbf{P}_{\textbf{S}}\) = 76.163, \(\textbf{Sn}_{\textbf{s}}\) = 82.096, \(\textbf{Sp}_{\textbf{s}}\) = 90.419, \(\textbf{F1}_{\textbf{s}}\) = 73.448) and InceptionV3 (\(\textbf{P}_{\textbf{S}}\) = 86.154, \(\textbf{Sn}_{\textbf{s}}\) = 90.667, \(\textbf{Sp}_{\textbf{s}}\) = 89.922, \(\textbf{F1}_{\textbf{s}}\) = 90.090). Thus, the proposed model seems more accurate and reliable for predicting the Sella structure. In order to assess the qualitative performance and efficacy of the suggested method, the study presents visual outcomes for the segmentation of regular, moderately altered, and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(\textbf{P}_{\textbf{S}}\) & \(\textbf{Sn}_{\textbf{s}}\) & \(\textbf{Sp}_{\textbf{s}}\) & \(\textbf{F1}_{\textbf{s}}\) \\ \hline VGG-19 & 76.163 & 82.096 & 90.419 & 73.448 \\ InceptionV3 & 86.154 & 90.667 & 89.922 & 90.090 \\ SellaMorph-Net & 96.832 & 98.214 & 97.427 & 97.314 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance Matrix for Predictive Analysis on Proposed Model, VGG19, and InceptionV3 model. Figure 7: Confusion Matrix for Four Classes of Lateral Cephalometric X-ray Images using (**a**) VGG19, (**b**) InceptionV3, and (**c**) Proposed SellaMorph-Net: Predicted vs. True Class. complexly altered ST structures in Figures 8-10. These structures were selected based on the cephalometric dataset's complexity. In addition, the study compared the accuracy of our hybrid SellaMorph-Net model against VGG-19 and InceptionV3. The results demonstrated that while VGG-19 and InceptionV3 were able to provide an approximate ST type, they were unable to predict the detailed morphological structures of ST.  
Figure 8: Qualitative results of VGG-19, InceptionV3, and proposed hybrid SellaMorph-Net on regular Sella Turcica subgroup dataset. Figure 9: Qualitative results of VGG-19, InceptionV3, and proposed hybrid SellaMorph-Net on moderately altered Sella Turcica subgroup dataset. On the other hand, SellaMorph-Net predicted the ST types with high precision for all three dataset subgroups. Furthermore, the complex altered ST structure in Figure 10 was evaluated, and it was found that SellaMorph-Net predicted the edges of these complex altered categories with greater accuracy and smoothness than the other two models. Further, the complex altered models were also able to predict the edges of these complex altered categories. Figure 10: Qualitative results of VGG-19, InceptionV3, and proposed hybrid SellaMorph-Net on complex altered Sella Turcica subgroup dataset. the proposed model accuracy is validated using 400 cephalometric dataset images available publicly, and the results show the significant performance of the proposed hybrid model in the discussion section (Table 7).