DGN-TBMF: DUAL GENERATOR NETWORK BASED ON TRI-BRAIN MODAL FUSION FOR ACCURATE BRAIN DISEASE DIAGNOSIS
DOI:
https://doi.org/10.59277/RRST-EE.2025.2.22Keywords:
Brain disease, Image fusion, Tri-modals, Deep learning, Fusion rules, Discrete Shearlet transformAbstract
Medical image fusion techniques are frequently used in a variety of applications. This fusion technology enables specialists to access images that incorporate anatomical and physiological data. It has been used in many clinical settings to fuse medical images of the brain for the diagnosis of brain diseases. Several methods have been proposed to fuse medical brain images, but these models need to be enhanced in terms of efficiency. This work employs a novel dual generator network-based tri-brain modal fusion (DGN-TBMF) framework to accurately predict brain diseases using tri-modality images, including MRI, CT, and PET. Initially, the gathered MRI and CT images are pre-processed using a scalable range-based adaptive bilateral (SCRAB) filter to reduce the noise artifacts. PET images are split into high and low-frequency components by the discrete shearlet transform (DST). The proposed DGN-TBMF approach comprises two generators and a detector module. The first Generator consists of dilated convolutional layers for extracting the relevant grey matter densities and cortical thickness from MRI and CT images. Similarly, the second generator extracts the relevant voxel intensities from PET images. The image fusion is performed using four fusion rules, and these images are taken as input to the deep learning-based detector for accurately detecting brain abnormalities. According to the experimental results, the proposed DGN-TBMF performed effectively in both quantitative and qualitative analyses, yielding respective values. The accuracy achieved by the proposed DGN-TBMF network is 99.25 % for dataset-1 and 99.04 % for dataset-2.
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