ArticleNeuroinformatics2024
A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer's Disease using MRI Images.
Article in Neuroinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis.Arquivos de neuro-psiquiatria · 2024Pooled it
- Dynamic F1-score-based voting strategies for multi-class classification: an adaptive ensemble approach for non-linear and imbalanced datasets.Scientific reports · 2026Article
- Ricci Flow-Based Approach for Early Diagnosis of Alzheimer's Disease.Neuroinformatics · 2026Article
- Applying ensemble machine learning techniques to MRI scans to predict Alzheimer's disease.Scientific reports · 2026Article
- Clinical pathways matter for multimodal deep learning in early Alzheimer's disease detection.Scientific reports · 2026Article
- A computational model to describe multi-regional brain architecture during neurodegeneration in Alzheimer's disease.Scientific reports · 2026Article
- Explainable artificial intelligence for early Alzheimer's diagnosis using enhanced grey relational features and multimodal data.Scientific reports · 2026Article
- A deep residual attention-Recurrent model for early and multi-stage Alzheimer's disease detection.Frontiers in aging neuroscience · 2026Article
- Ensemble Deep Learning Denoising (EDLD) model and optimized OTSU segmentation for Alzheimer's disease diagnosis using MRI images.Frontiers in artificial intelligence · 2026Article
- Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.Frontiers in neuroscience · 2026Review
- An artificial intelligence-based framework for Alzheimer's disease diagnosis from magnetic resonance imaging volumes via video vision transformer.Intelligent medicine · 2025Article
- Attention-driven hybrid deep learning and SVM model for early Alzheimer's diagnosis using neuroimaging fusion.BMC medical informatics and decision making · 2025Article
- Article
- Recent Advancements in Neuroimaging-Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e-Health: A Systematic Review.Health science reports · 2025Review
- Revolutionizing Alzheimer's disease detection with a cutting-edge CAPCBAM deep learning framework.Scientific reports · 2025Article
- Machine Learning-Based Alzheimer's Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Revolutionizing healthcare: a comparative insight into deep learning's role in medical imaging.Scientific reports · 2024Article
- Etiology of Late-Onset Alzheimer's Disease, Biomarker Efficacy, and the Role of Machine Learning in Stage Diagnosis.Diagnostics (Basel, Switzerland) · 2024Review
- Intelligent prediction of Alzheimer's disease via improved multifeature squeeze-and-excitation-dilated residual network.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Recently, the early diagnosis of Alzheimer's disease has gained major attention due to the growing prevalence of the disease and the resulting costs imposed on individuals and society. The main objective of this study was to propose an ensemble method based on deep learning for the early diagnosis of AD using MRI images. The methodology of this study consisted of collecting the dataset, preprocessing, creating the individual and ensemble models, evaluating the models based on ADNI data, and validating the trained model based on the local dataset. The proposed method was an ensemble approach selected through a comparative analysis of various ensemble scenarios. Finally, the six best individual CNN-based classifiers were selected to combine and constitute the ensemble model. The evaluation showed an accuracy rate of 98.57, 96.37, 94.22, 99.83, 93.88, and 93.92 for NC/AD, NC/EMCI, EMCI/LMCI, LMCI/AD, four-way and three-way classification groups, respectively. The validation results on the local dataset revealed an accuracy of 88.46 for three-way classification. Our performance results were higher than most reviewed studies and comparable with others. Although comparative analysis showed superior results of ensemble methods against individual architectures, there were no significant differences among various ensemble approaches. The validation results revealed the low performance of individual models in practice. In contrast, the ensemble method showed promising results. However, further studies on various and larger datasets are required to validate the generalizability of the model.
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Registered trials
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.