# Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica
## 4 Discussion  
Based on our knowledge, no prior research employed an SSL model with enhanced modules to categorize the extensive range of ST classes commonly encountered in clinical practice. Our SSLDSE model was evaluated against state-of-the-art SSL models trained on our dataset. However, it is important to note that this comparison was limited by variations in data samples and SSL technique parameters. The number of architectures developed by computational researchers is growing rapidly, especially those designed to support healthcare professionals. Various models, including U-Net, VGG19, ResNet34, Google-InceptionV3, ResNeXt50, and SellaMorph-Net (designed explicitly for ST structure segmentation), were introduced for the automated segmentation and classification of ST structures. In this section, the results of the proposed SSLDSE model are compared with state-of-the-art SSL models trained on our dataset as outlined in Table 3.  
The proposed SSLDSE method outperforms other SSL models in terms of binary classification effectiveness. The modified InceptionResNetV2 framework maintains a notable accuracy of 89.27% for bridging ST and 91.92% for non-bridging ST categories. Moreover, the active SSL technique demonstrates robust precision, attaining 81.67% for bridging ST and 85.10% for non-bridging ST groups. However, a more in-depth comparison of these methods reveals the distinct strengths and limitations for each approach. For instance, contrastive SSL is effective during initial feature extraction but struggles with ambiguous cases, particularly when negative samples are inadequately represented. Although GAN-based SSL has the potential to generate additional training data, it exhibits high variability in the classification results, especially for bridging cases. Active SSL shows promise in selecting informative samples but requires substantial manual intervention for labeling, which impacts its efficiency. In contrast, SSLDSE achieves superior performance by integrating deep subspace analysis with KL divergence, leading to higher accuracy and more stable results, particularly for the challenging bridging ST cases. Table 4 comprehensively analyzes various SSL methods compared to the proposed SSLDSE, including binary classification test outcomes from our proprietary dataset and the IEEE ISBI 2015 dataset. The accuracy levels reported by the methods in Table 4 range from 68.61% to 90.06% on the IEEE ISBI 2015 dataset and from 68.93% to 94.72% on our dataset. While our method exhibits lower accuracy compared to other state-of-the-art models, it shows higher AUC of ROC and F1 scores. Specifically, the proposed SSLDSE achieves AUC of ROC values of 91.02% for the IEEE ISBI 2015 dataset and 93.57% for our proprietary dataset. Additionally, the F1 scores for the proposed method are 92.08% and 95.37%, respectively. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Epochs**} & \multicolumn{2}{c}{**GAN**} & \multicolumn{2}{c}{**Contrastive**} & \multicolumn{2}{c}{**Active**} & \multicolumn{2}{c}{**MIncepResV2**} & \multicolumn{2}{c}{**SSLDSE**} \\  & **Brd** & **NBrd** & **Brd** & **NBrd** & **Brd** & **  
The authors employ stratified five-fold cross-validation to evaluate the performance of the proposed SSLDSE. Table 1 showcases the precision, recall, and F1 score for various epochs in the binary classification of bridging and non-bridging ST classes, respectively. Table 2 provides a comprehensive overview of accuracy, precision, recall, and F1 scores for classifying both bridging and non-bridging ST classes across different epochs. The results from Table 3 guide the selection of five-fold cross-validation for validating data samples not included in the training phase. Section 3.2. Experimental Validation Results of Classification elaborates on the validation test outcomes. Figure 5 illustrates (a) the confusion matrix and (b) the AUC-ROC curve for binary classification using the provided dataset.