Review on multi-modality medical image fusion using deep learning

Abstract

Multi-Modality medical image fusion is a method in which multiple images are merged having either single or multiple imaging modalities. This process is carried out to improve the quality of imaging while preserving all the essential and distinct features. Many areas such as Machine Learning, Artificial Intelligence, Image Processing, and Computer Vision are covered by Medical Image Fusion. This method has been adopted on a large scale by physicians to apprehend any damage or injury caused in organ tissues in clinical trials by performing a fusion of images with different modalities. In this review, Deep Learning methods carried out in the medical image fusion field have been discussed along with a comparison between their accuracies. The main objective of this paper is to list some of the most effective techniques in this domain and discuss their performance. At last, the paper concludes with the fact that although the development and growth in this area have increased over the years, many challenges have also come along the way.