ORIGINAL RESEARCH article
Front. Med. Technol.
Sec. Medtech Data Analytics
This article is part of the Research TopicMachine Learning for Medical Image AnalysisView all articles
An Advanced Multimodal Image Fusion Model for Accurate Detection of Alzheimer's Disease Using MRI and PET
Provisionally accepted
- 1Majmaah University, Al Majma'ah, Saudi Arabia
- 2Onaiza Colleges, Qassim, Saudi Arabia
- 3University of Tuscia, Viterbo, Italy
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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) imaging techniques. The proposed method leverages structural MRI and functional 18-fluorodeoxyglucose PET (FDG-PET) information derived from the AD 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 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.
Keywords: Alzheimer's disease (AD), multimodal image fusion, magnetic resonance imaging (MRI), Glowworm Swarm-optimized Spatial Multimodal Attention-enriched Convolutional Neural Network (GWS-SMAtt-ECNN), positron emission tomography (PET)
Received: 05 Sep 2025; Accepted: 05 Nov 2025.
Copyright: © 2025 Ansari, Mohammadi, Tassaddiq and Cattani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Asifa Tassaddiq
Carlo Cattani
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