ArticleBrain informatics2018
Brain MRI analysis for Alzheimer's disease diagnosis using an ensemble system of deep convolutional neural networks.
Article in Brain informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
90 citing papers in PubMed, 2 syntheses or guidelines pooled it, 455 citations in OpenAlex.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network.BioMed research international · 2022Pooled it
- Interpretable 2D Deep Learning for Alzheimer's Detection from sMRI: A Lightweight Residual CNN Approach with Comprehensive Preprocessing and Stratified Data Partitioning.Sensors (Basel, Switzerland) · 2026Article
- Applying ensemble machine learning techniques to MRI scans to predict Alzheimer's disease.Scientific reports · 2026Article
- A longitudinal and explainable 2.5D deep learning framework for Alzheimer's disease progression using ADNI MRI.Neuroimage. Reports · 2026Article
- Decision Strategies in AI-Based Ensemble Models in Opportunistic Alzheimer's Detection from Structural MRI.Journal of imaging informatics in medicine · 2026Article
- Comparative Analysis of Deep Learning Techniques in Alzheimer's Disease Diagnosis: Trends, Challenges, and Future Directions.MethodsX · 2026Review
- Classification of dysphagia severity after lateral medullary infarction with deep learning.Scientific reports · 2026Article
- Progression-Aware and Explainable CNN-Transformer Framework for Multiclass Alzheimer's Disease Staging Using MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Review of deep learning models for Alzheimer's disease detection: MRI-centric approaches and multimodal extensions.Frontiers in artificial intelligence · 2026Review
- Development of an explainable machine learning model for Alzheimer's disease prediction using clinical and behavioural features.MethodsX · 2025Article
- Trustworthy pneumonia detection in chest X-ray imaging through attention-guided deep learning.Scientific reports · 2025Article
- MedImg: An Integrated Database for Public Medical Images.Genomics, proteomics & bioinformatics · 2025Article
- Nanomedicine-Driven Modulation of the Gut-Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer's and Parkinson's Disease.International journal of molecular sciences · 2025Review
- Inductive and Transfer Learning-Based Hybrid Model Techniques for Accurate and Automated Diagnosis of Neurological Diseases.Brain and behavior · 2025Article
- A hybrid learning approach for MRI-based detection of alzheimer's disease stages using dual CNNs and ensemble classifier.Scientific reports · 2025Article
- Alzheimer's Disease Prediction Using Fisher Mantis Optimization and Hybrid Deep Learning Models.Diagnostics (Basel, Switzerland) · 2025Article
- Enhanced detection of mild cognitive impairment in Alzheimer's disease: a hybrid model integrating dual biomarkers and advanced machine learning.BMC geriatrics · 2025Article
- Machine learning applied to mild cognitive impairment: bibliometric and visual analysis from 2015 to 2024.Frontiers in neurology · 2025Review
- Advancing Alzheimer's Disease Diagnosis Using VGG19 and XGBoost: A Neuroimaging-Based Method.Current Alzheimer research · 2025Article
30 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Alzheimer's disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer's disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited by researchers for Alzheimer's disease diagnosis. Analyzing magnetic resonance imaging (MRI) is a common practice for Alzheimer's disease diagnosis in clinical research. Detection of Alzheimer's disease is exacting due to the similarity in Alzheimer's disease MRI data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a deep convolutional neural network for Alzheimer's disease diagnosis using brain MRI data analysis. While most of the existing approaches perform binary classification, our model can identify different stages of Alzheimer's disease and obtains superior performance for early-stage diagnosis. We conducted ample experiments to demonstrate that our proposed model outperformed comparative baselines on the Open Access Series of Imaging Studies dataset.
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What OpenQuestion holds
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.