A comprehensive review on transformer network for natural and medical image analysis

Abstract

The Transformer network is the main application area for natural language processing. It has gained traction lately and exhibits potential in the field of computer vision. This cutting-edge method has proven to offer a significant impact on image analysis, a crucial area of computer vision. The transformer's outstanding performance in vision computing places it as an alternative to the convolutional neural network for vision tasks. Transformers have taken center stage in the field of natural language processing. Despite the outstanding performance of transformer networks in natural image processing, their implementation in medical image analysis is gradually gaining roots. This study focuses on the transformer application in natural and medical image analysis. The first part of the study provides an overview of the core concepts of the attention mechanism built into transformers for long-range feature extraction. The study again highlights the various transformer architectures proposed for natural and medical image tasks such as segmentation, classification, image registration and diagnosis. Finally, the paper presents limitations identified in proposed transformer networks for natural and medical image processing. It also highlights prospective study opportunities for further research to better the computer vision domain, especially medical image analysis. This study offers knowledge to scholars and researchers studying computer vision applications as they focus on creating innovative transformer network-based solutions.