Smart healthcare is a framework that utilizes technologies such as wearable devices, the
Internet of Medical Things (IoMT), sophisticated machine learning algorithms, and wireless …

Multimodal fusion in neuroimaging combines data from multiple imaging modalities to
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …

In this paper, we proposed a new end-to-end model, termed as dual-discriminator
conditional generative adversarial network (DDcGAN), for fusing infrared and visible images …

The necessity of image fusion is growing in recently in image processing applications due to
the tremendous amount of acquisition systems. Fusion of images is defined as an alignment …

Multimodal medical image fusion consists in combining two or more images of the same or
different modalities aiming to improve the image content, and preserve information. The …

As an effective way to integrate the information contained in multiple medical images with
different modalities, medical image fusion has emerged as a powerful technique in various …

Multimodality medical imaging techniques have been increasingly applied in clinical
practice and research studies. Corresponding multimodal image analysis and ensemble …

Medical image fusion technique plays an an increasingly critical role in many clinical
applications by deriving the complementary information from medical images with different …

Cardiovascular illnesses are the leading cause of mortality worldwide, killing an estimated
27.9 million people each year, accounting for 31% of fatalities worldwide. Cardiovascular …

As a powerful assistance technique for biomedical diagnosis, multimodal medical image
fusion has emerged as a hot topic in recent years. Unfortunately, the trade-off among fusion …