Fusion of multi-modality biomedical images using deep neural networks

With the recent advancement in medical diagnostic tools, multi-modality images are extensively utilized as a lifesaving tool. An efficient fusion of can improve performance various tools. But, gathering all modalities for given patient is defined an ill-posed problem suffer from poor visibility and frequent dropout. Therefore, this paper, image model proposed to fuse images. To tune hyper-parameters model, multi-objective differential evolution used. The factor edge strength metrics form fitness function. Performance compared with nine competitive models over fifteen benchmark analyses reveal that outperforms models.