ArticleBiomedicines2023
Hybridized Deep Learning Approach for Detecting Alzheimer's Disease.
Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Comparative Analysis of Deep Learning Techniques in Alzheimer's Disease Diagnosis: Trends, Challenges, and Future Directions.MethodsX · 2026Review
- 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
- AI-driven framework for accurate detection of Alzheimer's disease in EEG.Scientific reports · 2026Article
- Alzheimer's disease detection from structural brain MRI using FFA U-Net segmentation and transfer learning.Frontiers in neuroscience · 2026Article
- Early diagnosis of alzheimer's disease using PET imaging and deep learning with comparative data augmentation techniques.Scientific reports · 2025Article
- A hybrid filtering and deep learning approach for early Alzheimer's disease identification.Scientific reports · 2025Article
- Deep ensemble learning with transformer models for enhanced Alzheimer's disease detection.Scientific reports · 2025Article
- A fine-tuned convolutional neural network model for accurate Alzheimer's disease classification.Scientific reports · 2025Article
- Biologically inspired hybrid model for Alzheimer's disease classification using structural MRI in the ADNI dataset.Frontiers in artificial intelligence · 2025Article
- GAN-enhanced deep learning for improved Alzheimer's disease classification and longitudinal brain change analysis.Frontiers in medicine · 2025Article
- Enhancing early detection of Alzheimer's disease through hybrid models based on feature fusion of multi-CNN and handcrafted features.Scientific reports · 2024Article
- A Feature-Fusion Technique-Based Alzheimer's Disease Classification Using Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians.Alzheimer's research & therapy · 2024Review
- Special Issue "Alzheimer's Disease-115 Years after Its Discovery".Biomedicines · 2024Article
- An Explainable AI Paradigm for Alzheimer's Diagnosis Using Deep Transfer Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Prediction of Alzheimer's disease stages based on ResNet-Self-attention architecture with Bayesian optimization and best features selection.Frontiers in computational neuroscience · 2024Article
- Machine learning models for diagnosing Alzheimer's disease using brain cortical complexity.Frontiers in aging neuroscience · 2024Article
- A combinatorial deep learning method for Alzheimer's disease classification-based merging pretrained networks.Frontiers in computational neuroscience · 2024Article
- Accurate Detection of Alzheimer's Disease Using Lightweight Deep Learning Model on MRI Data.Diagnostics (Basel, Switzerland) · 2023Article
- Review
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Authors and funding
5 authors.
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Abstract
Alzheimer's disease (AD) is mainly a neurodegenerative sickness. The primary characteristics are neuronal atrophy, amyloid deposition, and cognitive, behavioral, and psychiatric disorders. Numerous machine learning (ML) algorithms have been investigated and applied to AD identification over the past decades, emphasizing the subtle prodromal stage of mild cognitive impairment (MCI) to assess critical features that distinguish the disease's early manifestation and instruction for early detection and treatment. Identifying early MCI (EMCI) remains challenging due to the difficulty in distinguishing patients with cognitive normality from those with MCI. As a result, most classification algorithms for these two groups perform poorly. This paper proposes a hybrid Deep Learning Approach for the early detection of Alzheimer's disease. A method for early AD detection using multimodal imaging and Convolutional Neural Network with the Long Short-term memory algorithm combines magnetic resonance imaging (MRI), positron emission tomography (PET), and standard neuropsychological test scores. The proposed methodology updates the learning weights, and Adam's optimization is used to increase accuracy. The system has an unparalleled accuracy of 98.5% in classifying cognitively normal controls from EMCI. These results imply that deep neural networks may be trained to automatically discover imaging biomarkers indicative of AD and use them to identify the illness accurately.
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