An Unsupervised Image Fusion Approach Using Convolution Neural Network for Feature Enhancement of Medical Brain Images

Abstract

Image fusion is the process of amalgamation of two or more images into a single image that contains composite, enriched with diverse details from the original images. In the medical field, image fusion serves as an indispensable tool for elevating the precision of medical imagery and facilitating diagnostic processes. With the advent of deep learning, there has been a significant increase in the accuracy and effectiveness of image fusion techniques. This paper presents a deeplearningbased approach for medical image fusion that combines the advantages of deep-learning techniques with traditional image fusion methods. The proposed method is evaluated on medical data from different modalities, and the experimental results show that the proposed method outperforms existing state-of-the-art image fusion techniques.