Classification of Alzheimer disease using DenseNet-201 based on deep transfer learning technique

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 detectin...

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Bibliographic Details
Published in:PloS one Vol. 19; no. 9; p. e0304995
Main Authors: Zia-ur-Rehman, Awang, Mohd Khalid, Rashid, Javed, Ali, Ghulam, Hamid, Muhammad, Mahmoud, Samy F., Saleh, Dalia I., Ahmad, Hafiz Ishfaq
Format: Journal Article
Language:English
Published: United States Public Library of Science 06.09.2024
Public Library of Science (PLoS)
Subjects:
Accuracy
Aged
Aged, 80 and over
Alzheimer Disease - classification
Alzheimer Disease - diagnosis
Alzheimer Disease - diagnostic imaging
Alzheimer's disease
Artificial intelligence
Brain - diagnostic imaging
Brain - pathology
Brain research
Classification
Cognitive ability
Data augmentation
Datasets
Deep Learning
Dementia
Disease
Female
Humans
Identification and classification
Machine learning
Magnetic resonance
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Medical diagnosis
Medical imaging
Medical research
Medicine, Experimental
Neural networks
Neurodegenerative diseases
Neuroimaging
Neuroimaging - methods
Physiological aspects
Tomography
Transfer learning
Malaysia
Pakistan
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary: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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ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0304995