ArticleScientific reports2019
Classification and Visualization of Alzheimer's Disease using Volumetric Convolutional Neural Network and Transfer Learning.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 75 papers, 4 of them syntheses that pooled it.
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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
75 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial Intelligence in Diagnosing Depression Through Behavioural Cues: A Diagnostic Accuracy Systematic Review and Meta-Analysis.Journal of clinical nursing · 2026Pooled it
- Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis.Arquivos de neuro-psiquiatria · 2024Pooled it
- Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review.Alzheimer's research & therapy · 2021Pooled it
- Multi-dimensional attention framework for personalised Alzheimer's disease progression prediction across sporadic and genetic risk cohorts.Research square · 2026Article
- MRI neuroimaging-based Alzheimer's disease stage classification using deep neural network with convolutional block attention module and GAN-style noise injection.Scientific reports · 2026Article
- Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using a Vision Transformer and Hippocampal MRI Slices.Bioengineering (Basel, Switzerland) · 2026Article
- Explainable machine learning algorithm predicting working memory performance in Parkinson's disease using task-fMRI.Journal of neurology · 2025Article
- High-resolution conditional MR image synthesis through the PACGAN framework.Scientific reports · 2025Article
- Mind the Gap: Does Brain Age Improve Alzheimer's Disease Prediction?Human brain mapping · 2025Article
- Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography.BMC pulmonary medicine · 2025Article
- Convolutional variational auto-encoder and vision transformer hybrid approach for enhanced early Alzheimer's detection.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Enhancing Mild Cognitive Impairment Auxiliary Identification Through Multimodal Cognitive Assessment with Eye Tracking and Convolutional Neural Network Analysis.Biomedicines · 2025Article
- Enhanced detection of mild cognitive impairment in Alzheimer's disease: a hybrid model integrating dual biomarkers and advanced machine learning.BMC geriatrics · 2025Article
- Advancing Alzheimer's Disease Diagnosis Using VGG19 and XGBoost: A Neuroimaging-Based Method.Current Alzheimer research · 2025Article
- MRI-based whole-brain elastography and volumetric measurements to predict brain age.Biology methods & protocols · 2025Article
- Machine learning applied to mild cognitive impairment: bibliometric and visual analysis from 2015 to 2024.Frontiers in neurology · 2025Review
- Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury.Neuroinformatics · 2024Article
- [An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Improved neural network with multi-task learning for Alzheimer's disease classification.Heliyon · 2024Article
15 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, deep-learning-based approaches have been proposed for the classification of neuroimaging data related to Alzheimer's disease (AD), and significant progress has been made. However, end-to-end learning that is capable of maximizing the impact of deep learning has yet to receive much attention due to the endemic challenge of neuroimaging caused by the scarcity of data. Thus, this study presents an approach meant to encourage the end-to-end learning of a volumetric convolutional neural network (CNN) model for four binary classification tasks (AD vs. normal control (NC), progressive mild cognitive impairment (pMCI) vs. NC, stable mild cognitive impairment (sMCI) vs. NC and pMCI vs. sMCI) based on magnetic resonance imaging (MRI) and visualizes its outcomes in terms of the decision of the CNNs without any human intervention. In the proposed approach, we use convolutional autoencoder (CAE)-based unsupervised learning for the AD vs. NC classification task, and supervised transfer learning is applied to solve the pMCI vs. sMCI classification task. To detect the most important biomarkers related to AD and pMCI, a gradient-based visualization method that approximates the spatial influence of the CNN model's decision was applied. To validate the contributions of this study, we conducted experiments on the ADNI database, and the results demonstrated that the proposed approach achieved the accuracies of 86.60% and 73.95% for the AD and pMCI classification tasks respectively, outperforming other network models. In the visualization results, the temporal and parietal lobes were identified as key regions for classification.
Identifiers
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.