A Review of Transformer Networks in MRI Image Classification

Abstract

The urgent need to improve the efficiency and precision of analyzing images in the field of magnetic resonance imaging (MRI) has led to the rise of transformer networks, as a groundbreaking solution. This review delves deeply into how transformer networksre used in classifying MRI images summarizing studies to showcase the progress made and challenges faced in this evolving area. With their abilities transformers excel at analyzing MRI images by highlighting both details and functional complexities while effectively capturing both global and local nuances using their advanced handling of long distance connections. Additionally their adaptability allows for processing of input data in sizes along with the capability to break down processing and analysis tasks. By providing researchers with an insight into transformers and their crucial role in enhancing medical imaging methods this research aims to lay the groundwork, for advancements in this critical field.