## 3 Results
Furthermore, the study discovered that it was possible to achieve stable and high-score graphs for identifying single non-linear shapes in a specific subgroup of cephalometric radiographic data. However, the state-of-the-art models VGG-19 and InceptionV3 were less efficient on the cephalometric dataset, sometimes identifying outliers instead of actual morphological types and leading to false classification. In addition, the results demonstrate that SellaMorph-Net has a faster convergence of the loss function and significant mean pixel-wise IoU efficacy than the state-of-the-art models under the same training conditions. Furthermore, the proposed model's training and validation score variation is less, between \(\pm\)0.03 and \(\pm\)0.01, compared with the state-of-the-art VGG-19 and InceptionV3 (\(\pm\)0.07 and \(\pm\)0.03) models. These outcomes show that SellaMorph-Net is more reliable in identifying ST types in cephalometric images based on the specified colour scheme and class provided, which is crucial in accurately detecting different morphological types of ST.  
Table 5 presents a summary of the performance results achieved by three models on the cephalometric image dataset. The performance metrics included training and validation accuracy, mean/average IoU, and dice coefficient scores. Among the three models, SellaMorph-Net had the highest mean IoU at 71.29% and a dice coefficient of 73.24% in Sella-type segmentation, higher than VGG-19 and InceptionV3. The Dice Coefficient score measures how similar the predicted values are to the ground truth values and is identical to the IoU score. Additionally, SellaMorph-Net is a hybrid model that is hyperparameter-tuned on the cephalometric X-ray image subgroup datasets, which resulted in a higher IoU and dice score compared with the other models. We assessed the proposed SellaMorph-Net model performance through a comparative analysis with VGG19 and Inception V3, which are state-of-the-art models. The following performance matrix evaluates all the considered models (Precision (\(\mathbf{P_{S}}\)), Sensitivity (\(\mathbf{Sn_{s}}\)), Specificity (\(\mathbf{Sp_{s}}\)), and F1-score (\(\mathbf{F1_{s}}\))) shown in Figure 7 and Table 6. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & \multicolumn{2}{c}{**Bridging**} & \multicolumn{2}{c}{**Circular**} & \multicolumn{2}{c}{**Flat**} & \multicolumn{2}{c}{**Oval**} \\ \hline  & **Training** & **Validation** & **Training** & **Validation** & **Training** & **Validation** & **Training** & **Validation** \\  & **IoU** & **IoU** & **IoU** & **IoU** & **IoU** & **IoU** & **IoU** \\ \hline VGG-19 & 0.4372 \(\pm\) 0.07 & 0.4526 \(\pm\) 0.05 & 0.3668 \(\pm\) 0.04 & 0.3694 \(\pm\) 0.03 & 0.40 \(\pm\) 0.03 & 0.4327 \(\pm\) 0.03 & 0.2433 \(\pm\) 0.06 & 0.2447 \(\pm\) 0.03 \\ InceptionV3 & 0.4357 \(\pm\) 0.07 & 0.4381 \(\pm\) 0.06 & 0.3847 \(\pm\) 0.03 & 0.40 \(\pm\) 0.03 & 0.4325 \(\pm\) 0.03 & 0.3793 \(\pm\) 0.07 & 0.2976 \(\pm\) 0.03 & 0.2484 \(\pm\) 0.07 \\ SellaMorph- & 0.6539 \(\pm\) 0.03 & 0.6493 \(\pm\) 0.02 & 0.6370 \(\pm\) 0.01 & 0.5933 \(\pm\) 0.01 & 0.6517 \(\pm\) 0.01 & 0.5876 \(\pm\) 0.03 & 0.5663 \(\pm\) 0.02 & 0.5718 \(\pm\) 0.03 \\ Net & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Class based training and validation IoU results of VGG-19, InceptionV3, and SellaMorph-Net (proposed CNN approach). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Pixel-Wise IoU** & **Mean IoU** & **Dice Coefficient** \\ \hline  & & **Train IoU** &
\begin{tabular}{c} **Validation** \\ **IoU** \\ \end{tabular} & & & \\ \hline VGG-19 & 84.479 & 0.5637 & 0.5719 & 0.5974 & 0.6188 \\ InceptionV3 & 87.283 & 0.5654 & 0.5903 & 0.6029 & 0.6324 \\ SellaMorph-Net & 97.570 & 0.7329 & 0.6973 & 0.7129 & 0.7324 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance results of VGG-19, InceptionV3, and proposed models SellaMorph-Net. The findings demonstrate that SellaMorph-Net achieved significantly higher accuracy than the state-of-the-art models.