The accurate detection of Alzheimer's disease (AD), a progressive and irreversible neurodegenerative disorder, remains a critical challenge in clinical neuroscience. The research aims to develop an advanced multimodal image fusion model for the accurate detection of AD using positron emission tomography (PET) and magnetic resonance imaging (MRI) techniques. The proposed method leverages structural MRI and functional 18-fluorodeoxyglucose PET (FDG-PET) information derived from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). After preprocessing, including Gaussian filtering, skull stripping, and intensity normalization, voxel-based morphometry (VBM) is applied to extract gray matter (GM) features relevant to AD progression. A GM mask generated from MRI is used to isolate corresponding metabolic activity in the PET scans. These features are then integrated using a mask-coding strategy to construct a unified representation that captures both anatomical and functional characteristics. For classification, the model introduces a Glowworm Swarm-Optimized Spatial Multimodal Attention-Enriched Convolutional Neural Network (GWS-SMAtt-ECNN), where the optimization enhances both feature selection and network parameter tuning. The Python was implemented, and the result demonstrates that the proposed multimodal image fusion strategy outperforms traditional unimodal and basic fusion approaches in terms of F1-score (94.22%), recall (96.73%), and accuracy (98.70%). These results highlight the therapeutic usefulness of the suggested improved fusion architecture in facilitating immediate and accurate AD detection by MRI and PET.
Carlo Cattani (Tue,) studied this question.