## 4 Discussion  
The extended usage of computer-aided diagnostics (CAD) in healthcare highlights the need for a reliable and consistent system for cephalometric radiographs that can be continuously evaluated and applicable to potential medical diagnoses. This research presents a new hybrid approach to autonomous Sella structure segmentation in cephalometric radiographs. The proposed method employs an encoder-decoder CNN architecture with attention-gating modules (AGMs) and recurrent residual convolutional (RrCL) blocks, replacing traditional convolutional blocks. Extensive experimental evaluations show that the proposed hybrid encoder-decoder CNN architecture surpasses existing methods for non-linear structural segmentation. The figures presented in this study demonstrate the efficacy of the proposed method in segmenting complex non-linear structures of the ST in lat eph radiographs. The segmentation was successful for various classes of ST, including bridging, circular, flat, and oval in severely anomalous structures, indicating its potential for clinical prognostics. Additionally, the segmentation of the Sella was conducted on a dataset that included regular and complex subgroups, allowing for the evaluation of dataset combinations. The study utilised statistical methods such as mean IoU (Jaccard Index) and Dice Coefficient (Equations (5)-(8)) to evaluate the performance of the proposed and pre-trained models. Furthermore, the accuracy and pixel-wise IoU of each architecture were compared with determine which performed better. Tables 1-3 present the mean/mini-batch accuracy and time elapsed for three different subgroups of the SellaMorph-Net model: regular, moderately altered, and complex altered ST, respectively. The reported time elapsed refers to the inference time required for the model to predict each subset. Table 4 presents the training and validation IoU results for the state-of-the-art VGG-19 and InceptionV3 models and the proposed SellaMorph-Net model based on morphological classes of the Sella. The table also indicates the variation in training and validation accuracy using a pulse-minus (\(\pm\)) sign. The results of our study on the SellaMorph-Net model are presented in Table 5. It includes the scores for global accuracy, pixel-wise intersection-over-union (IoU) for training and validation, mean IoU, and dice coefficient. The findings were also compared with the results obtained under 5-fold k-cross-dataset settings. Our proposed method outperformed the pre-trained models VGG-19 and InceptionV3 in terms of mean IoU and dice coefficient scores, as presented in Table 5. In order to evaluate the effectiveness of the proposed model against other advanced models, we utilised performance matrices and created confusion matrix graphs, which helped us determine the model's robustness, as shown in Table 6 and Figure 7. The qualitative results from Figures 8-10 also support the quantitative findings, especially when evaluating the model in cross-dataset settings. The hybrid SellaMorph-Net model has several advantages over the InceptionV3 [26] and U-Net [32] models. It offers an end-to-end solution and has demonstrated remarkable \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Train Dataset** & **Test Dataset** & **IoU** & **Dice Coefficient** \\ \hline \multirow{2}{*}{VGG19} & \multirow{2}{*}{Our dataset} & Our dataset & 0.5703 & 0.5925 \\  & & IEEE ISBI dataset & 0.5753 & 0.5940 \\ \hline \multirow{2}{*}{InceptionV3} & \multirow{2}{*}{Our dataset} & Our dataset & 0.5913 & 0.6265 \\  & & IEEE ISBI dataset & 0.5897 & 0.6108 \\ \hline \multirow{2}{*}{Proposed} & \multirow{2}{*}{Our dataset} & Our dataset & 0.7314 & 0.7768 \\  & & IEEE ISBI dataset & 0.7864 & 0.8313 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative testing evaluation of state-of-the-art models and proposed approach on our dataset and publicly available dataset. accuracy in analysing full lat ceph radiographs.