ArticleIBRO neuroscience reports2022
Ensemble learning using traditional machine learning and deep neural network for diagnosis of Alzheimer's disease.
Article in IBRO neuroscience reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it, 80 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
- Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis.JMIR aging · 2024Pooled it
- Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review.Resuscitation · 2024Pooled it
- Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.Neural regeneration research · 2026Article
- Applying ensemble machine learning techniques to MRI scans to predict Alzheimer's disease.Scientific reports · 2026Article
- X-ViTCNN: A Novel Network-Level Fusion of Transfer Learning and Customized Vision Transformer for Multi-Stage Alzheimer's Disease Prediction Using MRI Scans.Diagnostics (Basel, Switzerland) · 2026Article
- Metaheuristic-driven dual-layer model for classifying Alzheimer's disease stages.Frontiers in computational neuroscience · 2026Article
- Alzheimer disease predicting from clinical and MRI data using DeepALZNET dual pathway framework.Scientific reports · 2025Article
- A novel approach hybrid of ensemble learning and 3-D CNN mechanism: early-stage diagnosis of Alzheimer's disease using EEG signals.Scientific reports · 2025Article
- Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging.BioMedInformatics · 2025Article
- An efficient ranking-based ensembled multiclassifier for neurodegenerative diseases classification using deep learning.Journal of neural transmission (Vienna, Austria : 1996) · 2025Article
- Generalized Super-Resolution 4D Flow MRI - Using Ensemble Learning to Extend Across the Cardiovascular System.IEEE journal of biomedical and health informatics · 2024Article
- Evaluating synthetic neuroimaging data augmentation for automatic brain tumour segmentation with a deep fully-convolutional network.IBRO neuroscience reports · 2024Article
- A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review.BMC medical imaging · 2024Review
- A comprehensive approach for osteoporosis detection through chest CT analysis and bone turnover markers: harnessing radiomics and deep learning techniques.Frontiers in endocrinology · 2024Article
- Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics.The world journal of men's health · 2024Review
- Enhancing healthcare recommendation: transfer learning in deep convolutional neural networks for Alzheimer disease detection.Frontiers in medicine · 2024Article
- Generalized super-resolution 4D Flow MRI-using ensemble learning to extend across the cardiovascular system.ArXiv · 2023Article
- Improving Structural MRI Preprocessing with Hybrid Transformer GANs.Life (Basel, Switzerland) · 2023Article
- ETISTP: An Enhanced Model for Brain Tumor Identification and Survival Time Prediction.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, Alzheimer's disease (AD) diagnosis using neuroimaging and deep learning has drawn great research attention. However, due to the scarcity of training neuroimaging data, many deep learning models have suffered from severe overfitting. In this study, we propose an ensemble learning framework that combines deep learning and machine learning. The deep learning model was based on a 3D-ResNet to exploit 3D structural features of neuroimaging data. Meanwhile, Extreme Gradient Boosting (XGBoost) machine learning was applied on a voxel-wise basis to draw the most significant voxel groups out of the image. The 3D-ResNet and XGBoost predictions were combined with patient demographics and cognitive test scores (Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR)) to give a final diagnosis prediction. Our proposed method was trained and validated on brain MRI brain images of the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. During the training phase, multiple data augmentation methods were employed to tackle overfitting. Our test set contained only baseline scans, i.e., the first visit scans since we aimed to investigate the ability of our approach in detecting AD during the first visit of AD patients. Our 5-fold cross-validation implementation achieved an average AUC of 100% during training and 96% during testing. Using the same computer, our method was much faster in scoring a prediction, approximately 10 min, than feature extraction-based machine learning methods, which often take many hours to score a prediction. To make the prediction explainable, we visualized the brain MRI image regions that primarily affected the 3D-ResNet model's prediction via heatmap. Lastly, we observed that proper generation of test sets was critical to avoiding the data leakage issue and ensuring the validity of results.
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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.