# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
## 2 Related Works  
In this section, we present the frameworks and algorithms pertinent to our study. This review of the literature focuses on the application of artificial intelligence, such as deep learning and machine learning, in the detection of COVID-19, with an emphasis on their roles in the processing and analysis of image datasets. Recently, wavelet-based super-resolution methods have gained attention for their ability to enhance fine-grained image features, which can be crucial for improving downstream medical diagnostic tasks. For instance, [PERSON] et al. [14] proposed a wavelet pyramid recursive neural network (WPRNN) constrained by wavelet energy entropy, which fuses both shallow and deep wavelet coefficient features across multiple resolutions to reconstruct high-quality medical images. This approach not only improves perceptual reconstruction quality but also preserves subtle structural details critical for accurate interpretation. [PERSON] et al. [7] proposed ClusterViG, a vision GNN that improves efficiency with Dynamic Efficient Graph Convolution (DEGC) by partitioning images, parallelizing graph construction, and integrating local-global feature learning. Additionally, it optimizes k-NN graph construction and enhances context awareness using GATv2. The results demonstrate 87.2% top-1 accuracy in ImageNet-1K, improved object detection and segmentation in MS COCO 2017, and up to 5x faster inference, making ClusterViG highly efficient and scalable for vision tasks. A transformer-based COVID-19 diagnosis framework that uses UNet for lung segmentation and Swin Transformer for classification was introduced by [PERSON] et al. [15]. Evaluated on MIA-COV19D, Swin Transformer achieved an F1-score of 0.935 (validation) and 0.84 (test), outperforming BiT-M and EfficientNetV2, with the latter excelling in precision (93.7%). TransGNN was proposed, a hybrid Transformer-GNN model for recommender systems that alternates Transformer and GNN layers to expand the receptive field while preserving structural information. Attention sampling, positional encoding, and efficient sample updates enhance efficiency and scalability. The model was evaluated on Yelp, Gowalla, Tmall, Amazon-Book, and MovieLens. TransGNN outperforms GNN and Transformer baselines, achieving up to 21.04% improvement in Recall@20 while reducing computational complexity, demonstrating its effectiveness and scalability in recommendation systems [16]. [PERSON] et al. [17] presented COVID-Transformer, a ViT-based model for COVID-19 detection using a 30K-image dataset (COVID-19, normal, pneumonia). It was trained with the NovoGrad optimizer and achieved 98% accuracy (AUC 99%) for binary and 92% accuracy (AUC 98%) for multi-class classification, outperforming CNN models. That is, Grad-CAM visualizations enhance interpretability for diagnosis. In the study in [9], Vision Transformers (ViTs) were evaluated for COVID-19 and diseased lung classification using Iran's COVID-19 CT dataset (377 patients) and Malaysia's dataset (81 diseased, 15 normal cases), where 32 x 32 patches achieved the highest accuracy (95.36%), while larger patches reduced performance. ViT outperformed CNN models (ResNet50, Xception) in efficiency, demonstrating its potential in medical imaging. [PERSON] et al. [4] introduced Contrastive Cross-Site Learning with Redesigned COVID-Net for COVID-19 CT classification, addressing domain shifts in multi-site datasets. The datasets include SARS-CoV-2 (2482 CT images) and COVID-CT (349 CT images), which incorporated domain-specific batch normalization and contrastive learning for feature extraction. The model achieved 96.24% AUC, 92.1% accuracy, 91.3% sensitivity, and 93.2% precision on SARS-CoV-2, and 85.32% AUC, 84.5% accuracy, 83.7% sensitivity, and 85.9% precision on COVID-CT. It outperforms standard COVID-Net by 12.16% and 14.23% in AUC and surpasses state-of-the-art multisite learning methods. [PERSON] et al. [18] presented TCGN, a Transformer-CNN-GNN model for gene expression prediction from histopathological images.