ArticleScientific reports2024
Explainable early detection of Alzheimer's disease using ROIs and an ensemble of 138 3D vision transformers.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A survey of deep learning techniques in detecting neurological disorders using MRI.Biomedical engineering online · 2026Pooled it
- Hybrid CNN-Transformer Framework for Molecular Imaging-based Early Alzheimer's Disease Detection.Molecular imaging and biology · 2026Article
- Inclusion of intracranial volume as a covariate feature improves MRI-based Alzheimer's disease classification.Magma (New York, N.Y.) · 2026Article
- Applying ensemble machine learning techniques to MRI scans to predict Alzheimer's disease.Scientific reports · 2026Article
- Vision and convolutional transformers for Alzheimer's disease diagnosis: a systematic review of architectures, multimodal fusion and critical gaps.Brain informatics · 2025Review
- A vision-language foundation model for Alzheimer's disease diagnosis using MRI and clinical data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Segmentation-Guided Development of Visual Classification Criteria for Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2025Article
- An artificial intelligence-based framework for Alzheimer's disease diagnosis from magnetic resonance imaging volumes via video vision transformer.Intelligent medicine · 2025Article
- TA-SSM net: tri-directional attention and structured state-space model for enhanced MRI-Based diagnosis of Alzheimer's disease and mild cognitive impairment.BMC medical imaging · 2025Article
- Early detection of Alzheimer's disease progression stages using hybrid of CNN and transformer encoder models.Scientific reports · 2025Article
- Article
- Explainable Artificial Intelligence in Neuroimaging of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Alzheimer's Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery.International journal of molecular sciences · 2025Review
- Deep learning techniques for Alzheimer's disease detection in 3D imaging: A systematic review.Health science reports · 2024Review
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Abstract
Early detection and accurate diagnosis of brain morphological abnormalities are essential for the effective management and treatment of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Structural magnetic resonance imaging (MRI) is a powerful support tool to aid in disease diagnosis and prediction. In this research study, we present an innovative approach to predict Alzheimer's disease (AD) and mild cognitive impairment (MCI) using MRI data, which integrates regional interest (ROI)-based methodology and deep learning within a comprehensible framework. The proposed method involves dividing the brain into 138 predetermined sections based on anatomical information. Next, we apply three-dimensional vision transformers (3D-ViTs) to each ROI individually, harnessing the power of deep learning. To improve prediction accuracy, we employ a deep belief network (DBN) as an ensemble learning model. Evaluating our approach on the baseline structural MRI dataset obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, and comparing it against five other competing models, we demonstrate its performance across four binary classification tasks and a three-class classification test (AD vs MCI vs CN (Cognitively Normal)). The proposed system outperforms existing models and provides interpretable insights into the brain regions that significantly contribute to solving each classification problem. Our findings align with the existing body of literature and hold promise for guiding future research directions in this domain.
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