volume | PIER Journals

Abstract

Alzheimer's disease (AD) is a brain degenerative disease, so the Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) of cerebral images are effective data in detecting the onset of the disease. In this work, a framework consisting of a cross attention multimodal fusion deep neural network and a patches weight optimization strategy is proposed. First, multiple points are randomly selected from the region of interest (ROI), and multiple 3-axial-plane patches are extracted centered on these points. Then, the patches from MRI and PET are fused using a fusion neural network to output diagnostic information for each patch. Finally, a weight is set for each patch; a particle swarm optimization algorithm is used to find the optimal weights for multiple patches; the diagnostic information from multiple patches is merged using these weights; and the final diagnostic results are output. The experiments on ADNI dataset show that this method has an accuracy of 94.03% in diagnosing AD and outperforms other methods of fusing MRI and PET data, which demonstrates the promising performance of this method.

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Dr. Weiming Lin

Dr. Shumin Feng

Dr. Hongfeng Wu

Dr. Jinlin Ma