Abstract
The development of artificial intelligence and machine learning has had a significant impact on medical diagnostics. This paper examines the application of the Vision Transformer (ViT) architecture for the task of classifying Computed Tomography (CT) images of the brain for the presence or absence of stroke signs. ViT which was originally developed for computer vision tasks, uses attention mechanisms, allowing the model to focus on important aspects of an image without first learning using task-specific data. The article presents the results of experiments on training the ViT model on a dataset of CT images, as well as performance comparisons with traditional methods such as Convolutional Neural Networks (CNNs). The results show that ViT can effectively classify strokes, demonstrating high accuracy and generalization ability based on new data. These findings may contribute to the wider application of transformer models in medical imaging which in the future may improve diagnostic accuracy and accelerate the treatment of stroke patients.