Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI

Topic: Amyloid, tau and blood-test diagnosis

The projected burden of dementia by Alzheimer's disease (AD) represents a looming healthcare crisis as the population of most countries grows older. Although there is currently no cure, it is possible to treat symptoms of dementia. Early diagnosis is paramount to the development and success of interventions, and neuroimaging represents one of the most promising areas for early detection of AD. We aimed to deploy advanced deep learning methods to determine whether they can extract useful AD biomarkers from structural magnetic resonance imaging (sMRI) and classify brain images into AD, mild cognitive impairment (MCI), and cognitively normal (CN) groups. We tailored and trained Convolutional Neural Networks (CNNs) on sMRIs of the brain from datasets available in online databases. Our proposed method, ADNet, was evaluated on the CADDementia challenge and outperformed several approaches in the prior art. The method's configuration with machine-learning domain adaptation, ADNet-DA, reached 52.3% accuracy. Contributions of our study include devising a deep learning system that is entirely automatic and comparatively fast, presenting competitive results without using any patient's domain-specific knowledge about the disease. We were able to implement an end-to-end CNN system to classify subjects into AD, MCI, or CN groups, reflecting the identification of distinctive elements in brain images. In this context, our system represents a promising tool in finding biomarkers to help with the diagnosis of AD and, eventually, many other diseases.

@article{folego2020-33195111,
  author  = {Folego, G. and Weiler, M. and Casseb, R. F. and Pires, R. and Rocha, A.},
  title   = {{Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI}},
  journal = {Frontiers in bioengineering and biotechnology},
  year    = {2020},
  doi     = {10.3389/fbioe.2020.534592},
  pmid    = {33195111},
  url     = {https://doi.org/10.3389/fbioe.2020.534592}
}

Key findings

• Contributions of our study include devising a deep learning system that is entirely automatic and comparatively fast, presenting competitive results without using any patient's domain-specific knowledge about the disease.

Why it matters

This study trained deep learning models on structural MRI brain images to classify them into Alzheimer's disease, mild cognitive impairment, and cognitively normal groups. One configuration of the method reached 52.3% accuracy on the CADDementia challenge.

New to this subject? Start with our plain-English guide: Digital health →

Study details

• Effect size52.3%
• Evidence strengthLow

Read the original

• DOI: 10.3389/fbioe.2020.534592
• PubMed: 33195111
• Original source →
• Free full text: Frontiers in Bioengineering and Biotechnology →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods Journal of Magnetic Resonance Imaging · 2008-02-26 · usefulness score 72/100 · Brain scans and imaging
• Alzheimer's disease neuroimaging initiative: a one-year follow up study using tensor-based morphometry correlating degenerative rates, biomarkers and cognition NeuroImage · 2009-04-01 · usefulness score 63/100 · Mild cognitive impairment
• The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease Alzheimer's & dementia : the journal of the Alzheimer's Association · 2011-04-21 · usefulness score 61/100 · Getting a diagnosis
• The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging‐Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease Alzheimer s & Dementia · 2011-04-22 · usefulness score 61/100 · Mild cognitive impairment
• The clinical use of structural MRI in Alzheimer disease Nature reviews. Neurology · 2010-02-01 · usefulness score 56/100 · Getting a diagnosis
• The Diagnosis of Mild Cognitive Impairment due to Alzheimer’s Disease: Recommendations from the National Institute on Aging-Alzheimer’s Association Workgroups on Diagnostic Guidelines for Alzheimer’s Disease FOCUS The Journal of Lifelong Learning in Psychiatry · 2013-01-01 · usefulness score 51/100 · Mild cognitive impairment
• Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry NeuroImage. Clinical · 2016-12-07 · usefulness score 51/100 · Long-term cohort studies
• A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease Brain sciences · 2020-02-05 · usefulness score 49/100 · Brain scans and imaging

Later studies that cite this one

1 study in the alzhub library lists this one in its references.

• Enhanced Deep Learning Framework for Precise MRI ‐Based Alzheimer's Disease Stage Classification Computational Intelligence · 2025-08-01 · usefulness score 31/100 · Brain scans and imaging

Related studies in Digital health tools

• Brain MRI Analysis for Alzheimer's Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning Sensors (Basel, Switzerland) · 2022-04-11 · usefulness score 51/100
• Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease NeuroImage. Clinical · 2016-12-18 · usefulness score 45/100
• Multiple Brain Tumor Classification with Dense CNN Architecture Using Brain MRI Images Life (Basel, Switzerland) · 2023-01-28 · usefulness score 43/100
• Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer’s Disease Classification Bioengineering · 2025-01-17 · usefulness score 43/100
• Real-time whole-brain imaging of hemodynamics and oxygenation at micro-vessel resolution with ultrafast wide-field photoacoustic microscopy Light, science & applications · 2022-05-17 · usefulness score 43/100
• Detection of emergency department patients at risk of dementia through artificial intelligence Alzheimer s & Dementia · 2025-06-01 · usefulness score 41/100

Also tagged “machine”

835 studies in the library carry the topic tag “machine”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 835 tagged studies.

• Tangle and neuron numbers, but not amyloid load, predict cognitive status in Alzheimer's disease Neurology · 2003-05-01 · usefulness score 58/100 · Cognitive tests and assessment
• Defining the Lowest Threshold for Amyloid-PET to Predict Future Cognitive Decline and Amyloid Accumulation Neurology · 2020-11-16 · usefulness score 59/100 · Biomarkers
• Development of [18F]ACI-19626 as a first-in-class brain PET tracer for imaging TDP-43 pathology Nature Communications · 2025-10-24 · usefulness score 49/100 · Brain tissue and pathology

Similar studies

• Early Detection of Alzheimers Disease Using Deep Learning 2025 · Brain scans and imaging
• Detection and Classification of Alzheimer’s Disease Using Deep and Machine Learning 2025 · Digital health tools
• Alzheimers and Brain Tumor Detection Using Deep Learning 2025 · Brain scans and imaging
• Alzheimer Detection Using Deep Learning Methods 2025 · Brain scans and imaging
• Optimized Neural Network Architectures for Early Detection of Alzheimer\'s disease Using MRI Images 2025 · Brain scans and imaging
• Deep Learning-Based Diagnosis of Alzheimer's Disease 2022 · Digital health tools

More on Digital health tools

• A review that covers this ground Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review Ageing research reviews · 2022-03-28 · usefulness score 62/100
• Newest in this subject Speaker-Wise Dementia Screening fromNaturalistic Speech with Lexical Entropy, Semantic Drift, and Multimodal Fusion Research Square · 2026-10-21 · Preprint — not yet peer-reviewed · usefulness score 16/100
• Newest in this subject Integrating Multisource Target Evidence with Machine-Learning Potency Prediction for Alzheimer’s Disease Drug Discovery bioRxiv · 2026-10-20 · Preprint — not yet peer-reviewed · usefulness score 16/100

See all studies in Digital health tools →