NEUROSCAN: Revolutionizing Brain Tumor Detection Using Vision-Transformer

Kamran Khan, Najam Aziz, Afaq Ahmad, Munib-ur-Rehman, Yasir Saleem Afridi ()
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Kamran Khan, Najam Aziz, Afaq Ahmad, Munib-ur-Rehman, Yasir Saleem Afridi: Department of Computer Systems Engineering, University of Engineering & Technology, Peshawar, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 5, 143-151

Abstract: Brain tumor detection is a pivotal component of neuroimaging, with significant implications for clinical diagnosis and patient care. In this study, we introduce an innovative deep-learning approach that leverages the cutting-edge Vision Transformer model, renowned for its ability to capture complex patterns and dependencies in images. Our dataset, consisting of 3000 images evenly split between tumor and non-tumor classes, serves as the foundation for our methodology. Employing Vision Transformer architecture, we processed high-resolution brain scans through patching and self-attention mechanisms. The model is trained through supervised learning to perform binary classification tasks. Our employed model achieved a high of 98.37% in tumor detection. While interpretability analysis was not explicitly performed, the inherent use of attention mechanisms in the Vision Transformer model suggests a focus on important brain regions and enhances its potential for prioritizing crucial information in brain tumor detection.

Keywords: Brain Tumor Detection; Medical Imaging; Classification; Vision Transformers; ViT; Machine Learning; Deep Learning (search for similar items in EconPapers)
Date: 2024
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