# Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica
## 3 Results
### Error Analysis  
The classification method that demonstrates the lowest error rate is recognized as the most effective. To gain a deeper understanding of the classification dynamics and the limitations of the proposed approach, a comprehensive error analysis is conducted. This analysis involves comparing the classification accuracy of the utilized SSL techniques, including contrastive, GAN, and active SSL, alongside a modified Inception-ResNet-V2 model against our proposed SSLDSE method. This analysis is supplemented by both statistical measures and visualization techniques to provide a complete view of the proposed method's behavior under different conditions. The analysis is initiated with a bar plot that  
Figure 7: t-SNE plots visualizing the quantitative assessment of the proposed SSLDSE method. The plots illustrate the effective separation between bridging and non-bridging labels from our proprietary and IEEE ISBI 2015 datasets, demonstrating a clear class distinction. includes error bars, which serves to assess the training accuracy and highlight potential misclassification risks. Furthermore, Figures 8-10 present the visual outcomes of the structure classifications, providing additional insights into the efficacy of the proposed method. As a statistical evaluation, shown in the boxplot with error bars depicted in Figure 8, the suggested method accurately classifies about 84 bridging cases in our dataset, resulting in a 16.00% error rate, indicating a moderate level of misclassification for bridging instances. In contrast, the model performs better with non-bridging cases, correctly identifying approximately 88 cases with a lower 12.00% error rate, demonstrating improved accuracy in distinguishing non-bridging instances within our dataset. Conversely, the performance on the IEEE ISBI 2015 dataset, depicted in Figure 8, reveals that the SSLDSE method correctly identifies roughly 78 bridging cases, yielding a higher 22.00% error rate. This suggests increased difficulty in accurately classifying bridging cases within this dataset. The model also correctly identifies about 87 non-bridging cases, with a 13.00% error rate, indicating slightly reduced accuracy compared to our dataset. In addition, Section 3.2. Experimental Validation Results of Classification, presents a t-SNE plot (Figure 7) that offers both visual and analytical insights into the model's classification capabilities, reinforcing the experimental results and clearly marking error-prone data points. The t-SNE plot displays distinct clusters with minimal overlap on our dataset, suggesting high classification accuracy and low error rates. For the IEEE dataset, however, the t-SNE plot reveals greater intermixing between classes, corresponding with the observed higher error rates and increased misclassifications. A more detailed examination of the errors reveals that the model struggles particularly with the subtle variations in the morphology of bridging ST cases. In instances where the bridging is less pronounced, or the anterior and posterior clinoid processes are only partially connected, the model often misclassifies these as non-bridging ST cases. This is especially evident in lower-quality images, where noise and lower resolution contribute to these misclassifications. The misclassification of these challenging bridging ST cases highlights a key limitation of the model in identifying complex, nuanced morphological features. To rigorously assess the efficacy of the implemented SSL methodologies in relation to the proposed approach, boxplots are created to visualize the classification results. Figure 9 showcases the boxplots for the SSLDSE technique and alternative SSL methods employed in this investigation, specifically for distinguishing between bridging and non-bridging ST categories.