## 3 Results  
This section accounts for the results obtained from the proposed method's quantitative and qualitative evaluation, utilising three subgroups of the collected cephalometric dataset. The cephalometric dataset was divided into three subgroups based on the complexity of the ST, ranging from regular to complex altered structures. These subgroups include regular ST, moderately altered ST, and complex altered ST, which require more detailed extraction. To obtain optimal results, different techniques of data pre-processing, augmentation, and hyper-parameter fine-tuning were implemented. The collected dataset consists of 1653 images, which is an odd number. Therefore, a split ration of 70:15:15 was applied for model evaluation, and a k-Fold cross validation method, where k size is five, was used for equal size distribution. The quantitative results compare how well the proposed method can identify different anatomical structures in datasets with varying levels of complexity in Sella features. Meanwhile, the qualitative analysis provides an observable illustration of the performance of the proposed method. The proposed hybrid SellaMorph-Net model's quantitative results are presented in Tables 1-3. This model was specifically designed to identify and classify different types of ST. The model reduces the input size of the images and then extracts features from the dataset to identify Sella types. The model was created from scratch using a consistent batch size of 4. It was optimised using AGM with a momentum value of 0.99. The learning rate for training was initially set at 0.001 and gradually decreased with each training epoch until it reached a specific number, which is 0.00001. The evaluation results of the proposed hybrid model on the regular ST subgroup are presented in Table 1. Table 2 shows the results for the moderately altered ST subgroup, and Table 3 presents the results for the complexly altered ST subgroup using the cephalometric radiographic dataset employed in this study. The experiments were conducted to evaluate the performance of the proposed model through a comparative study using two state-of-the-art pre-trained models, e.g., VGG-19 and InceptionV3, for identifying different morphological shapes of ST. According to Table 4, the proposed CNN model efficiently identifies various shapes of the Sella, ranging from regular to complex altered subgroups. This is achieved by distinguishing between bridging, circular, oval, and flat classes. \begin{table}
\begin{tabular}{c c c c c} \hline
**Epoch** & **Iteration** & \begin{tabular}{c} **Time Elapsed** \\ **(hh:mm:ss)** \\ \end{tabular} & \
\begin{tabular}{c} **Mini-Batch** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Base Learning** \\ **Rate** \\ \end{tabular} \\ \hline
1 & 1 & 00:02:57 & 86.67\% & 0.001 \\
10 & 310 & 00:20:07 & 98.93\% & 0.001 \\
20 & 620 & 00:42:29 & 99.07\% & 0.001 \\
30 & 930 & 01:04:59 & 99.13\% & 0.001 \\
40 & 1240 & 01:25:13 & 99.29\% & 0.0001 \\
50 & 1550 & 01:46:03 & 99.34\% & 0.0001 \\
60 & 1860 & 02:10:57 & 99.44\% & 0.0001 \\
70 & 2170 & 02:29:07 & 99.51\% & 0.00001 \\ \hline \end{tabular}
\end{table} 
Table 2: Evaluation metrics of the proposed SellaMorph-Net model for moderately altered ST subgroup of the dataset. \begin{table}
\begin{tabular}{c c c c c} \hline
**Epoch** & **Iteration** & \begin{tabular}{c} **Time Elapsed** \\ **(hh:mm:ss)** \\ \end{tabular} & \
\begin{tabular}{c} **Mini-Batch** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Base Learning** \\ **Rate** \\ \end{tabular} \\ \hline
1 & 1 & 00:02:42 & 84.27\% & 0.001 \\
10 & 310 & 00:21:27 & 98.64\% & 0.001 \\
20 & 620 & 00:43:57 & 98.73\% & 0.001 \\
30 & 930 & 01:07:48 & 98.97\% & 0.0001 \\
40 & 1240 & 01:27:03 & 99.15\% & 0.0001 \\
50 & 1550 & 01:50:00 & 99.23\% & 0.0001 \\
60 & 1860 & 02:13:53 & 99.37\% & 0.00001 \\
70 & 2170 & 02:32:17 & 99.49\% & 0.00001 \\ \hline \end{tabular}
\end{table} 
Table 3: Evaluation metrics of the proposed SellaMorph-Net model for complex altered ST subgroup of the dataset.