ArticleFrontiers in neuroscience2022
A transfer learning approach for multiclass classification of Alzheimer's disease using MRI images.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 53 citations in OpenAlex.
- Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.Neural regeneration research · 2026Article
- Alzheimer's disease detection from structural brain MRI using FFA U-Net segmentation and transfer learning.Frontiers in neuroscience · 2026Article
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- ConvNeXt-Driven Detection of Alzheimer's Disease: A Benchmark Study on Expert-Annotated AlzaSet MRI Dataset Across Anatomical Planes.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Alzheimer's Disease Diagnosis and Prognosis Using PET-MRI: A Narrative Review of High-Impact Literature Post-Tauvid Approval.Journal of clinical medicine · 2025Review
- Sensitivity analysis of a mathematical model of Alzheimer's disease progression unveils important causal pathways.Frontiers in neuroinformatics · 2025Article
- Development of a robust parallel and multi-composite machine learning model for improved diagnosis of Alzheimer's disease: correlation with dementia-associated drug usage and AT(N) protein biomarkers.Frontiers in neuroscience · 2024Article
- A combinatorial deep learning method for Alzheimer's disease classification-based merging pretrained networks.Frontiers in computational neuroscience · 2024Article
- Implications of Convolutional Neural Network for Brain MRI Image Classification to Identify Alzheimer's Disease.Parkinson's disease · 2024Article
- DenseIncepS115: a novel network-level fusion framework for Alzheimer's disease prediction using MRI images.Frontiers in oncology · 2024Article
- Deep CNN ResNet-18 based model with attention and transfer learning for Alzheimer's disease detection.Frontiers in neuroinformatics · 2024Article
- Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA.Healthcare (Basel, Switzerland) · 2023Article
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Authors and funding
8 authors at 7 institutions in 6 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's is an acute degenerative disease affecting the elderly population all over the world. The detection of disease at an early stage in the absence of a large-scale annotated dataset is crucial to the clinical treatment for the prevention and early detection of Alzheimer's disease (AD). In this study, we propose a transfer learning base approach to classify various stages of AD. The proposed model can distinguish between normal control (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD. In this regard, we apply tissue segmentation to extract the gray matter from the MRI scans obtained from the Alzheimer's Disease National Initiative (ADNI) database. We utilize this gray matter to tune the pre-trained VGG architecture while freezing the features of the ImageNet database. It is achieved through the addition of a layer with step-wise freezing of the existing blocks in the network. It not only assists transfer learning but also contributes to learning new features efficiently. Extensive experiments are conducted and results demonstrate the superiority of the proposed approach.
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