Brain MRI Analysis for Alzheimer's Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer's disease is the most common form of dementia and the fifth-leading cause of death among people over the age of 65. In addition, based on official records, cases of death from Alzheimer's disease have increased significantly. Hence, early diagnosis of Alzheimer's disease can increase patients' survival rates. Machine learning methods on magnetic resonance imaging have been used in the diagnosis of Alzheimer's disease to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on MRI images is complicated, requiring the involvement of an expert user. Therefore, implementing deep learning as an automatic feature extraction method could minimize the need for feature extraction and automate the process. In this study, we propose a pre-trained CNN deep learning model ResNet50 as an automatic feature extraction method for diagnosing Alzheimer's disease using MRI images. Then, the performance of a CNN with conventional Softmax, SVM, and RF evaluated using different metric measures such as accuracy. The result showed that our model outperformed other state-of-the-art models by achieving the higher accuracy, with an accuracy range of 85.7% to 99% for models with MRI ADNI dataset.

@article{alsaeed2022-35458896,
  author  = {AlSaeed, D. and Omar, S. F.},
  title   = {{Brain MRI Analysis for Alzheimer's Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning}},
  journal = {Sensors (Basel, Switzerland)},
  year    = {2022},
  doi     = {10.3390/s22082911},
  pmid    = {35458896},
  url     = {https://doi.org/10.3390/s22082911}
}

Key findings

• In addition, based on official records, cases of death from Alzheimer's disease have increased significantly.
• The result showed that our model outperformed other state-of-the-art models by achieving the higher accuracy, with an accuracy range of 85.7% to 99% for models with MRI ADNI dataset.

Why it matters

This study used a pre-trained CNN model to automatically extract features from MRI images for diagnosing Alzheimer's disease, then evaluated classification with Softmax, SVM, and random forest. The model achieved accuracy ranging from 85.7% to 99% on the ADNI dataset.

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

Study details

• Effect size85.7%
• Evidence strengthLow

Read the original

• DOI: 10.3390/s22082911
• PubMed: 35458896
• Original source →
• Free full text: Sensors →

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
• A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features Frontiers in neuroinformatics · 2020-06-11 · usefulness score 56/100 · Brain scans and imaging

Later studies that cite this one

3 studies in the alzhub library list this one in their references.

• Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images Scientific Reports · 2025-04-13 · usefulness score 44/100 · Brain scans and imaging
• Detection and Classification of Alzheimer’s Disease Using Deep and Machine Learning Tomography · 2025-12-26 · usefulness score 36/100 · Digital health tools
• AD-HOLDER: Alzheimer’s Disease Detection via Machine Learning based Histograms of Light GBM Classifier using MRI and PET Images Measurement Science Review · 2025-12-01 · usefulness score 31/100 · Brain scans and imaging

Related studies in Digital health tools

• Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022-04-07 · usefulness score 49/100
• Enhancing Alzheimer’s Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis Diagnostics · 2025-08-29 · usefulness score 43/100
• Multiple Brain Tumor Classification with Dense CNN Architecture Using Brain MRI Images Life (Basel, Switzerland) · 2023-01-28 · usefulness score 43/100
• Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia Neuroinformatics · 2016-07-01 · usefulness score 38/100
• Machine Learning-Based Framework for Differential Diagnosis Between Vascular Dementia and Alzheimer's Disease Using Structural MRI Features Frontiers in neurology · 2019-10-25 · usefulness score 46/100
• Brain MRI analysis for Alzheimer's disease diagnosis using an ensemble system of deep convolutional neural networks Brain informatics · 2018-05-31 · usefulness score 41/100

Also tagged “classification”

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

• Florbetaben PET imaging to detect amyloid beta plaques in Alzheimer's disease: Phase 3 study Alzheimer s & Dementia · 2015-03-27 · usefulness score 53/100 · Brain scans and imaging
• Ophthalmic imaging and artificial intelligence in early diagnosis of Alzheimer disease Neurology Bulletin · 2025-12-16 · usefulness score 27/100 · Brain scans and imaging
• 2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception Alzheimer's & dementia : the journal of the Alzheimer's Association · 2015-06-01 · usefulness score 69/100 · Long-term cohort studies

Similar studies

• Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI 2020 · Digital health tools
• AI-Driven Alzheimer’s Detection Performance Analysis of Pretrained CNN Models on MRI Data 2025 · Brain scans and imaging
• ENHANCED ALZHEIMER DISEASE DIAGNOSIS THROUGH DEEP LEARNING-BASED MRI SEGMENTATION AND CLASSIFICATION 2026 · Brain scans and imaging
• Classifying and diagnosing Alzheimer’s disease with deep learning using 6735 brain MRI images 2025 · Brain scans and imaging
• Optimized Neural Network Architectures for Early Detection of Alzheimer\'s disease Using MRI Images 2025 · Brain scans and imaging
• Performance evaluation of Alzheimer’s disease detection in brain MRI images using the Alzheimer-net classifier 2025 · Brain scans and imaging

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 →