ArticlePeerJ. Computer science2022
Alzheimer's disease diagnosis and classification using deep learning techniques.
Article in PeerJ. Computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 105 citations in OpenAlex.
- Deep Learning-Assisted Automated Diagnosis of Osteoporosis Based on Computed Tomography Scans: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis.Arquivos de neuro-psiquiatria · 2024Pooled it
- ACross-Paradigm CNN-Swin Transformer Ensemble with Super-Resolution Enhancement for Multi-Class Alzheimer's Disease Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Detection and Classification of Alzheimer's Disease Using Deep and Machine Learning.Tomography (Ann Arbor, Mich.) · 2025Article
- In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks.Journal of imaging informatics in medicine · 2025Article
- A novel hybrid deep learning model for segmentation and uzzy Res-LeNet based classification for Alzheimer's disease.Neurogenetics · 2025Article
- Classifying and diagnosing Alzheimer's disease with deep learning using 6735 brain MRI images.Scientific reports · 2025Article
- Cross study transcriptomic investigation of Alzheimer's brain tissue discoveries and limitations.Scientific reports · 2025Article
- EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models.Scientific reports · 2025Article
- Recent Advancements in Neuroimaging-Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e-Health: A Systematic Review.Health science reports · 2025Review
- RESIGN: Alzheimer's Disease Detection Using Hybrid Deep Learning based Res-Inception Seg Network.Current Alzheimer research · 2025Article
- An efficient method for early Alzheimer's disease detection based on MRI images using deep convolutional neural networks.Frontiers in artificial intelligence · 2025Article
- A hybrid transformer-based approach for early detection of Alzheimer's disease using MRI images.BioImpacts : BI · 2025Article
- Biologically inspired hybrid model for Alzheimer's disease classification using structural MRI in the ADNI dataset.Frontiers in artificial intelligence · 2025Article
- X-FASNet: cross-scale feature-aware with self-attention network for cognitive decline assessment in Alzheimer's disease.Frontiers in neurology · 2025Article
- Novel deep learning for multi-class classification of Alzheimer's in disability using MRI datasets.Frontiers in bioinformatics · 2025Article
- Deep learning of structural MRI predicts fluid, crystallized, and general intelligence.Scientific reports · 2024Article
- Novel hippocampus-centered methodology for informative instance selection in Alzheimer's disease data.Heliyon · 2024Article
- Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer's disease.Scientific reports · 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
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease is an incurable neurodegenerative disease that affects brain memory mainly in aged people. Alzheimer's disease occurs worldwide and mainly affects people aged older than 65 years. Early diagnosis for accurate detection is needed for this disease. Manual diagnosis by health specialists is error prone and time consuming due to the large number of patients presenting with the disease. Various techniques have been applied to the diagnosis and classification of Alzheimer's disease but there is a need for more accuracy in early diagnosis solutions. The model proposed in this research suggests a deep learning-based solution using DenseNet-169 and ResNet-50 CNN architectures for the diagnosis and classification of Alzheimer's disease. The proposed model classifies Alzheimer's disease into Non-Dementia, Very Mild Dementia, Mild Dementia, and Moderate Dementia. The DenseNet-169 architecture outperformed in the training and testing phases. The training and testing accuracy values for DenseNet-169 are 0.977 and 0.8382, while the accuracy values for ResNet-50 were 0.8870 and 0.8192. The proposed model is usable for real-time analysis and classification of Alzheimer's disease.
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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.