# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
## 4 Results and Discussion
### Results  
Table 2 presents a comparative analysis of the performance metrics of ViTGNN, the proposed model, against several established baseline models, evaluated using four key metrics: accuracy, precision, recall, and F1-score. These metrics collectively evaluate the model's ability to detect COVID-19 correctly, avoid false positives, and minimize false negatives while providing a harmonic balance between precision and recall. The baseline models include various configurations such as COVID-NET (Single and Joint), redesigned versions, Series and Parallel Adapters, MS-NET, and a Contrastive model. Each baseline represents different approaches to feature extraction and classification, showing varying performance levels. The proposed ViTGNN demonstrates superior performance in all metrics, achieving an accuracy of 95.98%, with a precision of 96.07%, a recall of 96.01%, and an F1-score of 95.98%. These results surpass all baseline models by significant margins. Among the baseline models, the best-performing approach, the Contrastive model, achieves an accuracy of 90.83%, with a precision of 95.75%, a recall of 85.89%, and an F1-score of 90.97%. Although the Contrastive model exhibits a strong balance between precision and recall, it still falls short of the performance of ViTGNN, especially in recall and the F1-score, highlighting the effectiveness of the proposed model in achieving a more comprehensive generalization. Interestingly, other baseline models such as MS-NET and the redesigned Single COVID-NET also perform well, with MS-NET achieving an accuracy of 87.98% and the redesigned Single COVID-NET achieving 89.09%. However, these models show limitations in precision and recall, indicating challenges in balancing the trade-offs between false positives and false negatives. In addition, joint models consistently underperform compared to their single counterparts, suggesting potential overfitting or noise amplification when combining features. Similarly, the Series Adapter outperforms the Parallel Adapter, which indicates structural differences in how the adapters handle feature hierarchies. The loss curve for training and validation, illustrated in Figure 3, further supports the effectiveness of the ViTGNN training process. The steady decline in training loss, accompanied by a consistent reduction in validation loss, indicates that the model effectively generalizes without overfitting. Furthermore, the training and validation accuracy curve in Figure 4 shows a convergence towards high accuracy, with a minimal divergence between training and validation accuracy, highlighting the robustness of the model during training. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-score (\%)** & **AUC (\%)** \\ \hline Single (COVID-NET [4]) & 77.12 & 80.04 & 70.97 & 76.03 & 84.08 \\ Single (Redesign [4]) & 89.09 & 94.58 & 83.78 & 88.97 & 94.12 \\ Joint (COVID-NET [4]) & 68.72 & 68.27 & 69.41 & 69.17 & 74.12 \\ Joint (Redesign [4]) & 78.42 & 80.82 & 74.07 & 77.86 & 85.72 \\ Series Adapter [4] & 85.73 & 90.98 & 81.91 & 86.19 & 92.93 \\ Parallel Adapter [4] & 82.13 & 83.51 & 80.02 & 82.39 & 89.99 \\ MS-NET [4] & 87.98 & 93.78 & 84.91 & 88.73 & 94.37 \\ SepNorm [4] & 88.76 & 95.46 & 82.97 & 87.88 & 94.57 \\ Contrastive [4] & 90.83 & 95.75 & 85.89 & 90.97 & 96.24 \\
**ViTGNN (Proposed)** & **95.98** & **96.07** & **96.01** & **95.98** & **98.69** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance metrics comparison of ViTGNN and baseline models. Figure 3: Training and Validation Loss for ViTGNN Model. The ROC curve in Figure 5 provides further evidence of ViTGNN's superior classification capabilities. The model achieves an AUC score of 0.9869, indicating exceptional performance in distinguishing between classes. This high AUC score reflects the ability of ViTGNN to minimize false positives and false negatives, which is essential in high-stakes applications such as medical imaging.