TWO SCALE DECOMPOSITION BASED MULTI MODEL MEDICAL IMAGE FUSION USING DEEP LEARNING

Medical image fusion is the process of combining information from several imaging modalities to improve the accuracy of diagnoses. In this study, we present an innovative method for multi-modal medical picture fusion that integrates a two-scale decomposition strategy with deep learning. The method breaks down input images into low- and high-frequency parts to keep both big structural elements and little details from diverse types of imaging, such MRI, CT, and PET. Low-frequency components capture the overall shape of the body, whereas high-frequency components save important textures and edges. We use deep convolution neural networks (CNNs) and attention processes to automatically figure out the best way to combine different types of information at each scale. This makes sure that the information is integrated correctly. The treated parts are put together to make the fused image, then post-processing methods are utilize to make the image better. We use the best parts of both scale decomposition and deep learning to make multi-model picture fusion work better by adding more structural detail and reducing noise. The proposed method works better than traditional fusion methods, as shown by experimental findings that show better image quality and more accurate diagnostic information. This method has a lot of potential for use in medicine, where high-quality, multi-modal images are very important for making educated choices.