Medical image fusion using convolutional neural network

Medical image fusion methods combine medical pictures from many morphologies to improve the accuracy and reliability of medical diagnoses, and they are becoming more significant in a variety of clinical applications. This research introduces a convolutional neural network (CNN) based medical image fusion approach to create a fused picture with good visual quality and clear structural details. To generate a weight map, the proposed technique employs a trained Siamese convolutional network to fuse the pixel activity information of source pictures. Meanwhile, the original picture is decomposed using a contrast pyramid. Source pictures are combined using distinct spatial frequency bands and a weighted fusion operator. The suggested fusion method can successfully maintain the exact structural information of source pictures and generate excellent human visual effects, according to the findings of comparison trials.