ArticlePloS one2024
Classification of Alzheimer disease using DenseNet-201 based on deep transfer learning technique.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Machine Learning-Assisted Classification of Pathogenic Yeasts Using Laser Light Scattering and Conventional Microscopy.Journal of imaging · 2026Article
- Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.Frontiers in neuroscience · 2026Review
- A novel neuroimaging based early detection framework for alzheimer disease using deep learning.Scientific reports · 2025Article
- Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer's Disease Classification.Brain sciences · 2025Article
- Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging.BioMedInformatics · 2025Article
- Recent Advancements in Neuroimaging-Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e-Health: A Systematic Review.Health science reports · 2025Review
- Quantum AI for psychiatric diagnosis: enhancing dementia classification with quantum machine learning.Frontiers in psychiatry · 2025Article
- Correction: Classification of Alzheimer disease using DenseNet-201 based on deep transfer learning technique.PloS one · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a brain illness that causes gradual memory loss. AD has no treatment and cannot be cured, so early detection is critical. Various AD diagnosis approaches are used in this regard, but Magnetic Resonance Imaging (MRI) provides the most helpful neuroimaging tool for detecting AD. In this paper, we employ a DenseNet-201 based transfer learning technique for diagnosing different Alzheimer's stages as Non-Demented (ND), Moderate Demented (MOD), Mild Demented (MD), Very Mild Demented (VMD), and Severe Demented (SD). The suggested method for a dataset of MRI scans for Alzheimer's disease is divided into five classes. Data augmentation methods were used to expand the size of the dataset and increase DenseNet-201's accuracy. It was found that the proposed strategy provides a very high classification accuracy. This practical and reliable model delivers a success rate of 98.24%. The findings of the experiments demonstrate that the suggested deep learning approach is more accurate and performs well compared to existing techniques and state-of-the-art methods.
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