ArticleScientific reports2025
A deep learning model for early diagnosis of alzheimer's disease combined with 3D CNN and video Swin transformer.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Leveraging Advanced AI Frameworks for Dual PPAR α/γ Agonist Discovery in Alzheimer's Disease.ACS chemical neuroscience · 2026Review
- Learning heritable multimodal brain representation via contrastive learning.Research square · 2026Article
- Learning heritable multimodal brain representation via contrastive learning.bioRxiv : the preprint server for biology · 2026Article
- Advances in AI-based diagnosis of Alzheimer's disease using MRI: a comprehensive survey.Frontiers in medicine · 2026Review
- Lightweight 3D CNN for MRI Analysis in Alzheimer's Disease: Balancing Accuracy and Efficiency.Journal of imaging · 2025Article
- Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends.Biosensors · 2025Review
- Gender-based Alzheimer's detection using ResNet-50 and binary dragonfly algorithm on neuroimaging.Frontiers in artificial intelligence · 2025Article
- APLG-Net: an anatomy-guided local-global hybrid network with progression-aware supervision for structural MRI-based NC/MCI/AD classification.Frontiers in neurologyArticle
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Authors and funding
7 authors.
Funding
Abstract
Alzheimer's disease (AD) constitutes a neurodegenerative disorder predominantly observed in the geriatric population. If AD can be diagnosed early, both in terms of prevention and treatment, it is very beneficial to patients. Therefore, our team proposed a novel deep learning model named 3D-CNN-VSwinFormer. The model consists of two components: the first part is a 3D CNN equipped with a 3D Convolutional Block Attention Module (3D CBAM) module, and the second part involves a fine-tuned Video Swin Transformer. Our investigation extracts features from subject-level 3D Magnetic resonance imaging (MRI) data, retaining only a single 3D MRI image per participant. This method circumvents data leakage and addresses the issue of 2D slices failing to capture global spatial information. We utilized the ADNI dataset to validate our proposed model. In differentiating between AD patients and cognitively normal (CN) individuals, we achieved accuracy and AUC values of 92.92% and 0.9660, respectively. Compared to other studies on AD and CN recognition, our model yielded superior results, enhancing the efficiency of AD diagnosis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.