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| Home > Publications Database > Hybrid multimodal late fusion frameworks for bvFTD classification in imbalanced dementia datasets. |
| Home > Publications Database > Hybrid multimodal late fusion frameworks for bvFTD classification in imbalanced dementia datasets. |
| Journal Article | DZNE-2026-01007 |
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2026
Frontiers Research Foundation
Lausanne
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Please use a persistent id in citations: doi:10.3389/fnagi.2026.1892568
Abstract: Behavioral variant frontotemporal dementia (bvFTD) is an irreversible neurodegenerative disorder characterized by progressive changes in personality and behavior. Magnetic Resonance Imaging (MRI) is widely used to detect and assess structural brain alterations associated with the disease. However, due to the low prevalence of bvFTD among neurodegenerative diseases causing the dementia syndrome, conventional machine learning approaches may struggle to capture comprehensive feature representations. Therefore, this study proposes two late fusion frameworks that integrate a 3D convolutional neural network and a multilayer perceptron (MLP) for improved bvFTD diagnosis.A total of 5,928 participants were included, comprising 3,415 healthy controls (HC), 2,276 Alzheimer's disease (AD), and 237 bvFTD, resulting in a class imbalanced setting with bvFTD as the minority class. To address class imbalance, bvFTD data were initially augmented. A 3D-DenseNet was used to extract features from 3D T1-weighted MRI scans, while an MLP-based model was applied to regional brain volumetric measurements obtained from automated MRI-based brain segmentation. Twelve CNN models with different hyperparameter configurations were trained. Models with and without data augmentation, as well as two fusion-based approaches, were evaluated using accuracy, F1-score, and area under the curve (AUC).Both fusion strategies improved accuracy, F1-score, and AUC compared to the baseline model without data augmentation. Notable improvement was also observed for the bvFTD class, with up to a 120% increase in F1-score. In one of the fusion frameworks, an accuracy of 0.95 ± 0.01 was achieved for bvFTD vs. HC classification. The results demonstrate the effectiveness of the fusion-based approaches compared to non-fused models, outperforming several state-of-the-art methods.The proposed frameworks demonstrate that data augmentation and fusion strategies can improve accuracy, F1-score, and AUC, with statistically significant gains. Overall, the frameworks improve diagnostic performance and support the identification of relevant biomarkers associated with bvFTD pathology.
Keyword(s): 3D-CNN ; bvFTD ; classification ; late fusion ; magnetic resonance imaging
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