# Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica
## 3 Results
### Experimental Validation Results of Classification  
The IEEE ISBI 2015 dataset is used along with our dataset to evaluate the effectiveness of the model and to reduce the risk of overfitting. The IEEE ISBI 2015 dataset comprises 400 lateral cephalogram X-ray images. This approach for validating the proposed SSLDSE model is considered innovative. A 5-fold cross-validation is performed, and the resulting confusion matrix is shown in Figure 6.  
For our dataset, the model achieves 83.18% precision for the bridging ST class and 84.62% for the non-bridging ST class. The recall rates are 87.50% for the bridging ST class and 88% for the non-bridging ST class. The F1 scores reach 85.29% for the bridging ST class  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Epochs** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline
50 & 98.50\% & 97.25\% & 96.80\% & 98.50\% \\
100 & 96.75\% & 95.40\% & 94.20\% & 96.30\% \\
150 & 97.10\% & 96.75\% & 95.00\% & 97.90\% \\
200 & 97.90\% & 95.85\% & 94.85\% & 97.25\% \\
250 & 96.25\% & 96.00\% & 96.10\% & 98.10\% \\
300 & 97.95\% & 94.50\% & 94.50\% & 97.60\% \\ Average & 97.28\% & 96.12\% & 95.65\% & 97.77\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results present the overall SSLDSE method scores for binary ST classification, highlighting the model’s effectiveness with average accuracy, precision, recall, and F1 score metrics for bridging and non-bridging classes.  
Figure 6: Confusion matrix and ROC curve showcasing the validation results of the binary classifier, highlighting the proposed model’s classification performance through true positive/negative rates and the AUC-ROC score.  
and 86.28% for the non-bridging ST class, resulting in an overall average accuracy of 86%. This underscores the model's effectiveness. Similar performance is observed on the IEEE ISBI dataset, with 87% precision for the bridging ST class and 85.23% for the non-bridging ST class. The recall rates are 77.68% for the bridging ST class and 75% for the non-bridging ST class. F1 scores for both classes are 82.04% and 79.79%, respectively, resulting in an overall accuracy of 81.50%. These results further confirm the robustness and reliability of the model, as illustrated by the ROC curve in Figure 6, for the external dataset.  
In addition to the quantitative assessment, a t-SNE plot is employed to visualize the learned data embeddings from both the dataset and the IEEE ISBI dataset in a reduced-dimensional space. As shown in Figure 7, the t-SNE plot provides a qualitative evaluation of the model's ability to distinguish between bridging and non-bridging ST classes. The plot reveals distinct clusters, validating the model's effective separation of the two classes based on their characteristics. These clusters support the quantitative results regarding precision, recall, and F1 scores while demonstrating the model's capacity to generalize to unseen data.  
However, despite these positive outcomes, several areas for improvement remain. The model struggles with subtle bridging cases, particularly where the anterior and posterior clinoid processes are only partially connected. Future work could focus on enhancing feature learning for these challenging cases by incorporating domain-specific knowledge or spatial features. Additionally, better pre-processing techniques, such as super-resolution methods, could improve the handling of low-quality images. Fine-tuning the model on various bridging subtypes and employing ensemble learning methods may further enhance its robustness. Active learning could also be utilized to select the most informative bridging cases for labeling, addressing the issue of a limited dataset. Lastly, improving cross-dataset generalization through domain adaptation techniques would enhance performance across diverse datasets.