# Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica
## 4 Discussion
The model demonstrates a validation accuracy of 86%, which closely matches the testing accuracy of 93.57%. This alignment suggests effective model generalization without overfitting. Additionally, the (c) confusion matrix and (d) AUC-ROC curve reveal a validation accuracy of 81.50% on the IEEE ISBI 2015 dataset, comparable to the testing accuracy of 91.02%. These results further confirm that the SSLDSE model avoids overfitting and performs well on unseen data. The t-SNE plot in Figure 6 offers a visual assessment of the ability of the proposed SSLDSE to differentiate between classes. The distinct clustering observed in the plot validates the effectiveness of the model in distinguishing between the two classes based on their feature representations. The proposed SSLDSE model's limitations and potential areas for enhancement are elucidated through a comprehensive error analysis. Figure 9 presents visual interpretations of classification errors, revealing a higher prevalence of misclassifications within the bridging ST class, which suggests the model's heightened sensitivity to certain structural intricacies. Further investigation of these errors using confusion matrices and AUC-ROC curves, as shown in Figure 5, demonstrates that despite the high overall accuracy of the proposed SSLDSE, specific error patterns underline the model's struggle in distinguishing between structurally similar entities. This examination of errors allows for the identification of areas where the model could be improved, such as through enhanced data augmentation techniques or more advanced feature extraction methods, ultimately boosting its capacity to differentiate between subtle morphological variations. According to the existing literature and the author's knowledge, no dedicated system has been specifically developed for classifying ST morphological structures using the enhanced modules proposed here. First, the model utilizes a fine-tuned InceptionResNetV2 for feature extraction. Then, the SSLDSE model is evaluated using a five-fold stratified cross-validation approach, which optimizes image selection for training and testing. This method minimizes bias by ensuring that each image is used four times for training and once for testing. Furthermore, the model is validated with 627 images separated from the author's dataset that are not included in the training phase. The validation results show consistent accuracy, confirming that the model does not exhibit overfitting. A more detailed analysis can be found in Section 3.2. Experimental Validation Results of Classification. The AdamW optimization method, which combines the advantages of weight decay and Adam, is employed. Unlike the standard Adam optimizer, AdamW separates weight decay from the gradient update, leading to better generalization. Recent studies often favor \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**AUC-ROC**} & \multicolumn{2}{c}{**F1 Score**} \\  & **IEEE** & **Our** & **IEEE** & **Our** & **IEEE** & **Our** \\ \hline GAN SSL & 68.61\% & 68.93\% & 64.88\% & 65.73\% & 62.86\% & 67.01\% \\ Contrastive SSL & 70.66\% & 73.11\% & 74.40\% & 74.07\% & 72.06\% & 73.17\% \\ Active SSL & 84.92\% & 85.64\% & 83.73\% & 83.00\% & 83.00\% & 85.07\% \\ MInceepResV2 & 85.07\% & 86.12\% & 85.97\% & 84.03\% & 83.02\% & 82.00\% \\ Proposed SSLDSE & 90.06\% & 92.72\% & 91.02\% & 93.57\% & 92.08\% & 95.37\% \\ \hline \hline \end{tabular} Note: MInceepResV2—Modified Inception-ResNet-v2.  
\end{table} 
Table 4: Accuracy, AUC of ROC, and F1 score of the proposed SSLDSE method are validated and compared with state-of-the-art methods through blind testing on the IEEE ISBI 2015 and proprietary datasets, demonstrating the outstanding performance of SSLDSE across both datasets. AdamW for its superior performance compared to traditional optimizers like RMSProp.