# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
## 4 Results and Discussion  
### Baseline Models on SARS-CoV-2 Dataset  
[PERSON] et al. [4] conducted a comprehensive evaluation of multiple models on the SARS-CoV-2 CT dataset (Site A), which comprises 2482 CT images from 120 patients. In particular, this is the same dataset that we used to train and evaluate our proposed ViTGNN model. Their research provides benchmarks for assessing the effectiveness of different model designs in the diagnosis of COVID-19 in the cross-domain using CT imaging. Initially designed for chest X-ray imaging, the **Single (COVID-Net0)** model was applied directly to CT scans without architectural modifications. As a result, it showed limited performance, achieving the accuracy of 77.12%, precision of 80.04%, recall of 70.97%, F1-score of 76.03%, and an AUC of 84.08%. Recognizing these limitations, [PERSON] et al proposed the **Single (Redesign)** model which introduced architectural and training enhancements, including selective batch normalization layers to stabilize feature distributions, global average pooling to reduce model parameters, and a cosine annealing learning rate strategy to facilitate smoother convergence. These improvements significantly improved model performance across all metrics. In an effort to address dataset variability, the **Joint (COVID-Net)** model was trained jointly on data combined from multiple clinical sites without considering domain shifts. However, this naive merging approach led to a performance degradation with the model attaining the accuracy of 68.72%, precision of 68.27%, recall of 69.41%, F1-score of 69.17%, and an AUC of 74.78%. With an enhanced architecture in the **Joint (Redesign)** model, performance remained limited due to persistent challenges related to inter-site discrepancies. This resulted in an accuracy of 78.42%, precision of 80.82%, recall of 74.07%, F1-score of 77.86%, and an AUC of 85.72%. In order to mitigate domain heterogeneity, [PERSON] et al introduced domain-adaptive architectures such as the **Series Adapter** model. This model sequentially inserted domain-specific adaptive layers after convolutional blocks, enabling limited site-specific adjustments while maintaining a shared backbone. As a result, it achieved improved performance with the accuracy of 85.73%, precision of 90.98%, recall of 81.91%, F1-score of 86.19%, and an AUC of 92.93%. Alternatively, the **Parallel Adapter** model placed domain-adaptive convolutional filters alongside the main convolution operations within residual blocks. This parallel structure allowed the network to learn domain-specific and shared features simultaneously, achieving an accuracy of 82.13%, precision of 83.51%, recall of 80.02%, F1-score of 82.39%, and an AUC of 89.99%. Further advancing domain generalization, the **MS-Net** (Multi-Site Network) architecture was developed, which combined domain-specific auxiliary branches with a primary shared backbone and used an online knowledge transfer mechanism during training. This design further improved generalization across datasets, achieving an accuracy of 87.98%, precision of 93.78%, recall of 84.91%, F1-score of 88.73%, and an AUC of 94.37%, albeit at the expense of increased model complexity and computational overhead. Another notable contribution was the **SepNorm** (Separate Normalization) model, which addressed domain heterogeneity by replacing standard batch normalization with domain-specific batch normalization (DSBN) layers. In this setup, each clinical site maintained independent normalization statistics during training and inference, eliminating statistical mismatches between domains. This simple yet powerful modification resulted in an accuracy of 88.76%, precision of 95.46%, recall of 82.97%, F1-score of 87.88%, and an AUC of 94.57%. Finally, the best performance was achieved with the **Contrastive** model.