Hybrid multimodal late fusion frameworks for bvFTD classification in imbalanced dementia datasets.
Authors
Affiliations (35)
Affiliations (35)
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.
- Institute and Policlinic of Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, Rostock, Germany.
- Department of Psychiatry and Neurosciences, Charité Universitätsmedizin Berlin, Berlin, Germany.
- ECRC Experimental and Clinical Research Center, Charité Universitätsmedizin Berlin, Berlin, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.
- Neuropsychiatry and Laboratory of Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Department of Psychiatry and Psychotherapy, School of Medicine and Health, Technical University of Munich, and German Center for Mental Health (DZPG), Munich, Germany.
- University of Edinburgh and UK DRI, Edinburgh, United Kingdom.
- Department of Parkinson's, Sleep and Movement Disorders, Centre for Neurology, University Hospital Bonn, Bonn, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
- Department of Neurology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany.
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Goettingen, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany.
- Neurosciences and Signaling Group, Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal.
- Leibniz Institute for Neurobiology, Magdeburg, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany.
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom.
- Medical Faculty, Clinic for Neurology, University Hospital Magdeburg, Magdeburg, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.
- Department of Neurology, University Hospital of Munich, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany.
- Munich Cluster for Systems Neurology (SyNergy) Munich, Munich, Germany.
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany.
- Department of Neurology, University Medical Centre, Rostock, Germany.
- Department of Neurology, Translational Neurodegeneration Section "Albrecht Kossel", University Medical Centre, Rostock, Germany.
- Division of Translational Genomics of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tuebingen, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany.
- Department of Vascular Neurology, University Hospital Bonn, Bonn, Germany.
- Department of Old Age Psychiatry and Cognitive Disorders, University Hospital Bonn, University of Bonn, Bonn, Germany.
- Berlin Center for Advanced Neuroimaging, Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Department of Cognitive Neurology, MR-Research in Neurosciences, University Medical Center Goettingen, Göttingen, Germany.
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tuebingen, Germany.
- Department of Radiology, University Hospital of Munich, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany.
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.