<![CDATA[Ischemic stroke is a medical condition that requires rapid and accurate diagnosis because it can cause permanent brain damage. Medical images, such as CT scans, can serve as a source of information to assist healthcare professionals in identifying patients' brain conditions. However, manual image analysis is time-consuming and may be affected by human limitations in image interpretation. This study aims to develop a CT scan image classification application to assist in identifying ischemic stroke using the Vision Transformer (ViT) method. The research stages include data preprocessing, patch embedding, positional encoding, transformer encoder, and classification head to classify images into two categories: stroke and normal. The dataset consists of patient CT scan images obtained from Hendra Hospital, Binjai. The system was implemented using the Python programming language and the FastAPI framework. The evaluation results show that the ViT model achieved an accuracy of 82.93% in the final testing. These results indicate that the ViT model can be used to classify CT scan images into stroke and normal categories on the dataset used in this study. This research may serve as an approach for developing deep learning-based decision support systems to assist in the analysis of medical images.]]>
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