Fusion of multi-modality biomedical images using deep neural networks

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