ArticleDiagnostics (Basel, Switzerland)2022
An MRI Scans-Based Alzheimer's Disease Detection via Convolutional Neural Network and Transfer Learning.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 104 citations in OpenAlex.
- Lightweight Vision Transformer with transfer learning for interpretable Alzheimer's disease severity assessment.Scientific reports · 2025Article
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- Fusion of transfer learning models for detection of alzheimer's disease using bidirectional long short-term memory with equilibrium optimization algorithm.Scientific reports · 2025Article
- An optimized hybrid deep learning model to detect Alzheimer disease.Scientific reports · 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
- Enhancing automated detection and classification of dementia in individuals with cognitive impairment using artificial intelligence techniques.Scientific reports · 2025Article
- FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images.Scientific reports · 2025Article
- Advancing Alzheimer's Disease Diagnosis Using VGG19 and XGBoost: A Neuroimaging-Based Method.Current Alzheimer research · 2025Article
- Effective deep convolutional neural network with attention mechanism for Alzheimer disease classification.Frontiers in radiology · 2025Article
- Novel deep learning for multi-class classification of Alzheimer's in disability using MRI datasets.Frontiers in bioinformatics · 2025Article
- Technological Advancements in Augmented, Mixed, and Virtual Reality Technologies for Surgery: A Systematic Review.Cureus · 2024Review
- Deep Learning for Alzheimer's Disease Prediction: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2024Review
- A dynamic fuzzy rule-based inference system using fuzzy inference with semantic reasoning.Scientific reports · 2024Article
- An Explainable AI Paradigm for Alzheimer's Diagnosis Using Deep Transfer Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Detection of Alzheimer's disease using Otsu thresholding with tunicate swarm algorithm and deep belief network.Frontiers in physiology · 2024Article
- Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges.Bioengineering (Basel, Switzerland) · 2023Article
- Inception Convolution and Feature Fusion for Person Search.Sensors (Basel, Switzerland) · 2023Article
- Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection.Cancers · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 3 countries.
Funding
Abstract
Alzheimer’s disease (AD) is the most common type (>60%) of dementia and can wreak havoc on the psychological and physiological development of sufferers and their carers, as well as the economic and social development. Attributed to the shortage of medical staff, automatic diagnosis of AD has become more important to relieve the workload of medical staff and increase the accuracy of medical diagnoses. Using the common MRI scans as inputs, an AD detection model has been designed using convolutional neural network (CNN). To enhance the fine-tuning of hyperparameters and, thus, the detection accuracy, transfer learning (TL) is introduced, which brings the domain knowledge from heterogeneous datasets. Generative adversarial network (GAN) is applied to generate additional training data in the minority classes of the benchmark datasets. Performance evaluation and analysis using three benchmark (OASIS-series) datasets revealed the effectiveness of the proposed method, which increases the accuracy of the detection model by 2.85−3.88%, 2.43−2.66%, and 1.8−40.1% in the ablation study of GAN and TL, as well as the comparison with existing works, respectively.
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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.