ArticleCognitive computation2022
Deep Learning Approach for Early Detection of Alzheimer's Disease.
Article in Cognitive computation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled it.
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Who cites it
59 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.Journal of medical systems · 2023Pooled it
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- Alzheimer's and Parkinson's Detection with Video-Based Hybrid Deep Learning from Brain MRI.Journal of imaging informatics in medicine · 2026Article
- Predictive modeling for early diagnosis of dementia using sequential data analysis and data mining.Scientific reports · 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
- GGDA-net: geometry-guided deformable attention network for Alzheimer's disease image classification.Frontiers in neuroscience · 2026Article
- Artificial intelligence techniques for classification of Alzheimer's disease using neuroimaging data: a review.Frontiers in artificial intelligence · 2026Review
- Metaheuristic-driven dual-layer model for classifying Alzheimer's disease stages.Frontiers in computational neuroscience · 2026Article
- Computational Approaches to Neurological Disorder Diagnosis: An In-Depth Review of Current Methods and Future ProspectsCurrent medical imaging · 2026Review
- Development of Alzheimer's disease risk score for future integrated primary care: a white-box approach.Frontiers in aging neuroscience · 2026Article
- Detection and Classification of Alzheimer's Disease Using Deep and Machine Learning.Tomography (Ann Arbor, Mich.) · 2025Article
- Early diagnosis of alzheimer's disease using PET imaging and deep learning with comparative data augmentation techniques.Scientific reports · 2025Article
- Fusion of transfer learning models for detection of alzheimer's disease using bidirectional long short-term memory with equilibrium optimization algorithm.Scientific reports · 2025Article
- Comparing deep learning and Fourier series models for equipment failure prediction in predictive industrial maintenance 4.0.Scientific reports · 2025Article
- Development of Alzheimer's Disease Risk Score for Future Primary Care: A White-Box Approach.medRxiv : the preprint server for health sciences · 2025Article
- An ensemble model based on transfer learning for the early detection of Alzheimer's disease.Scientific reports · 2025Article
- An optimized hybrid deep learning model to detect Alzheimer disease.Scientific reports · 2025Article
- Predicting Alzheimer's Disease Progression from Sparse Multimodal Data by NeuralODE Models.bioRxiv : the preprint server for biology · 2025Article
- A Convolutional Mixer-Based Deep Learning Network for Alzheimer's Disease Classification from Structural Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Recent Advancements in Neuroimaging-Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e-Health: A Systematic Review.Health science reports · 2025Review
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a chronic, irreversible brain disorder, no effective cure for it till now. However, available medicines can delay its progress. Therefore, the early detection of AD plays a crucial role in preventing and controlling its progression. The main objective is to design an end-to-end framework for early detection of Alzheimer's disease and medical image classification for various AD stages. A deep learning approach, specifically convolutional neural networks (CNN), is used in this work. Four stages of the AD spectrum are multi-classified. Furthermore, separate binary medical image classifications are implemented between each two-pair class of AD stages. Two methods are used to classify the medical images and detect AD. The first method uses simple CNN architectures that deal with 2D and 3D structural brain scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset based on 2D and 3D convolution. The second method applies the transfer learning principle to take advantage of the pre-trained models for medical image classifications, such as the VGG19 model. Due to the COVID-19 pandemic, it is difficult for people to go to hospitals periodically to avoid gatherings and infections. As a result, Alzheimer's checking web application is proposed using the final qualified proposed architectures. It helps doctors and patients to check AD remotely. It also determines the AD stage of the patient based on the AD spectrum and advises the patient according to its AD stage. Nine performance metrics are used in the evaluation and the comparison between the two methods. The experimental results prove that the CNN architectures for the first method have the following characteristics: suitable simple structures that reduce computational complexity, memory requirements, overfitting, and provide manageable time. Besides, they achieve very promising accuracies, 93.61% and 95.17% for 2D and 3D multi-class AD stage classifications. The VGG19 pre-trained model is fine-tuned and achieved an accuracy of 97% for multi-class AD stage classifications.
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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.