# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
## 1 Introduction  
The COVID-19 pandemic has had a profound impact on healthcare systems around the world and emphasizes the urgent need for effective, rapid, and reliable diagnostic tools. Traditionally, Real-Time Polymerase Chain Reaction (RT-PCR) has been the primary method for detecting SARS-CoV-2 infection. However, despite being the gold standard, RT-PCR has notable limitations: It is time-consuming, prone to false negatives, and highly dependent on the quality of the sample and the timing of collection. This has led to an increasing interest in alternative diagnostic methods that can offer faster and potentially more reliable results, especially in clinical settings of high demand [1, 2]. Computed Tomography (CT) imaging has emerged as a promising complementary diagnostic tool for COVID-19, primarily due to its high sensitivity in detecting COVID-19 related lung abnormalities such as ground-glass opacities, septal thickening, and consolidations. Unlike RT-PCR, which directly detects viral genetic material, CT imaging provides a visual representation of disease progression, capturing structural changes in lung tissue. Studies have shown that CT scans can reveal abnormalities even in cases with low viral load where RT-PCR might yield a false negative, making CT a valuable tool for timely and accurate diagnosis [3; 4; 5]. The application of artificial intelligence (AI), particularly deep learning, has transformed medical imaging by enabling automated analysis and interpretation of complex visual data. Convolutional Neural Networks (CNNs) have been widely adopted for image analysis and have demonstrated considerable success in detecting lung abnormalities associated with COVID-19. However, CNNs have limitations when it comes to capturing complex spatial dependencies in an image, which are crucial for understanding nuanced patterns in medical imaging [6]. Vision Transformers (ViTs), which leverage self-attention mechanisms, offer a solution by modeling global dependencies across an image, allowing them to identify broader patterns that may indicate the presence of COVID-19. Despite their strengths, ViTs can sometimes overlook local structural relationships, which are essential to detect fine-grained features in medical images, such as small lesions or subtle tissue changes specific to COVID-19. Several research studies have explored the application of Graph Neural Networks (GNNs) and Vision Transformers (ViTs) in medical imaging and classification tasks; however, no existing research has explicitly combined GNNs and ViTs for SARS detection. GNNs are effective in capturing spatial and topological relationships within the data, making them suitable for structural pattern recognition in images. For instance, ClusterViG combined CNNs with GNNs for improved feature representation [7]. On the other hand, ViTs have shown strong performance in medical imaging, especially in the classification of COVID-19 using chest CT scans [8], and researchers have also investigated the impact of different patch sizes in ViTs for the classification of lung diseases, highlighting their ability to model global dependencies in image analysis [9]. Existing SARS-CoV-2 detection frameworks predominantly rely on CNN-based deep learning models or ViTs independently without leveraging GNNs for spatial and relational inference. This suggests that a Vision Transformer and Graph Neural Network (ViTGNN) architecture could introduce a novel and effective method to improve the diagnostic accuracy of SARS-CoV-2 detection from CT scans. The rationale behind the combination of ViT and GNN (ViTGNN) is based on their complementary strengths. The GNN captures relationships and structural patterns in data [10], while the ViT excels at processing visual information [11; 12]. This hybrid process allows ViTGNN to overcome the individual limitations of CNNs and ViTs [13], offering a more comprehensive approach to the diagnosis of COVID-19. The key contributions of this paper are as follows.