ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2025
Early detection of Alzheimer's disease using deep learning methods.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Small-Data Deep Learning for Alzheimer-Spectrum Classification from Structural MRI: A Feasibility Study Using OASIS.Journal of imaging · 2026Article
- Engineering Nanoplatforms for Alzheimer's Disease Detection via Biomolecular Corona Proteomic and Lipidomic Profiling.Advanced healthcare materials · 2026Article
- Applying ensemble machine learning techniques to MRI scans to predict Alzheimer's disease.Scientific reports · 2026Article
- Clinical impact of the Alzheimer's Disease Neuroimaging Initiative: A review of studies using ADNI data (2023 to June 2025).Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- Applying Multiple Machine Learning Models to Classify Mild Cognitive Impairment from Speech in Community-Dwelling Older Adults.Journal of Intelligence · 2026Article
- Decoding Dementia Mechanisms: Identification of Key Oligodendrocyte- Associated Genes through Integrative Bioinformatics and Machine Learning.Current topics in medicinal chemistry · 2026Article
- Review of deep learning models for Alzheimer's disease detection: MRI-centric approaches and multimodal extensions.Frontiers in artificial intelligence · 2026Review
- TriFusion-ADFormer: a deep learning framework for early Alzheimer's disease detection using MRI and cognitive metrics.Frontiers in artificial intelligence · 2026Article
- Investigating Alzheimer's Disease Progression Using a Radiomics Approach: The Hippocampal-Amygdala Border in FDG-Positron Emission Tomography Scans.Neuro-degenerative diseases · 2026Article
- Design of a deep learning prediction model for Alzheimer's and Parkinson's Disease using MRI images.Frontiers in artificial intelligence · 2026Article
- Article
- PredatorNet: a clinically trustworthy deep learning framework for multi-class Alzheimer's disease classification using MRI.Frontiers in aging neuroscience · 2026Article
- Early detection of Alzheimer's disease using deep learning methods.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
introductionAlzheimer's disease (AD), a leading cause of dementia, requires early detection for effective intervention. This study employs AI to analyze multimodal datasets, including clinical, biomarker, and neuroimaging data, using hybrid deep learning frameworks to improve predictive accuracy.
methodsA novel framework was developed, including trained models for structured data and magnetic resonance images. The structured data model used a long short-term memory (LSTM) for temporal dependencies and a feedforward neural network (FNN) for static patterns. The MRI-based model employed ResNet50 and MobileNetV2 to extract spatial features. Models were applied on National Alzheimer's Coordinating Centre (NACC) and Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets and compared to previous works.
resultsThe MRI-based model achieved 96.19% accuracy on the ADNI dataset, while the hybrid model attained 99.82% accuracy on NACC dataset. DISCUSSION: This study highlights hybrid AI models' potential in AD detection, enabling earlier interventions and improved detection outcomes. HIGHLIGHTS: AI models were explored for early AD detection using NACC and ADNI datasets. Achieved high accuracy with LSTM on NACC data, showing potential for early AD diagnosis. Evaluated transfer learning models (MobileNetV2, ResNet-50) to address data limitations. A method is proposed for the careful validation of transfer learning models in medical brain diagnostics.
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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.