TELKOMNIKA Telecommunication Computing Electronics and Control
Ensemble learning approach for multi-class classification of Alzheimer’s stages using magnetic resonance imaging
Dublin Core
Title
TELKOMNIKA Telecommunication Computing Electronics and Control
Ensemble learning approach for multi-class classification of Alzheimer’s stages using magnetic resonance imaging
Ensemble learning approach for multi-class classification of Alzheimer’s stages using magnetic resonance imaging
Subject
Alzheimer’s disease
Convolutional neural network
Ensemble learning
Mild cognitive imapirement
non convertible
Mild conginitive imapirement
convertible
Pre-trained models
Convolutional neural network
Ensemble learning
Mild cognitive imapirement
non convertible
Mild conginitive imapirement
convertible
Pre-trained models
Description
Alzheimer’s disease (AD) is a gradually progressing neurodegenerative
irreversible disorder. Mild cognitive impairment convertible (MCIc) is the
clinical forerunner of AD. Precise diagnosis of MCIc is essential for
effective treatments to reduce the progressing rate of the disease. The other
cognitive states included in this study are mild cognitive impairment
non-convertible (MCInc) and cognitively normal (CN). MCInc is a stage in
which aged people suffer from memory problems, but the stage will not lead
to AD. The classification between MCIc and MCInc is crucial for the early
detection of AD. In this work, an algorithm is proposed which concatenates
the output layers of Xception, InceptionV3, and MobileNet pre-trained
models. The algorithm is tested on the baseline T1-weighted structural
magnetic resonance imaging (MRI) images obtained from Alzheimer’s
disease neuroimaging initiative database. The proposed algorithm provided
multi-class classification accuracy of 85%. Also, the proposed algorithm
gave an accuracy of 85% for classifying MCIc vs MCInc, an accuracy of
94% for classifying AD vs CN, and an accuracy of 92% for classifying
MCIc vs CN. The results exhibit that the proposed algorithm outruns other
state-of-the-art methods for the multi-class classification and classification
between MCIc and MCInc.
irreversible disorder. Mild cognitive impairment convertible (MCIc) is the
clinical forerunner of AD. Precise diagnosis of MCIc is essential for
effective treatments to reduce the progressing rate of the disease. The other
cognitive states included in this study are mild cognitive impairment
non-convertible (MCInc) and cognitively normal (CN). MCInc is a stage in
which aged people suffer from memory problems, but the stage will not lead
to AD. The classification between MCIc and MCInc is crucial for the early
detection of AD. In this work, an algorithm is proposed which concatenates
the output layers of Xception, InceptionV3, and MobileNet pre-trained
models. The algorithm is tested on the baseline T1-weighted structural
magnetic resonance imaging (MRI) images obtained from Alzheimer’s
disease neuroimaging initiative database. The proposed algorithm provided
multi-class classification accuracy of 85%. Also, the proposed algorithm
gave an accuracy of 85% for classifying MCIc vs MCInc, an accuracy of
94% for classifying AD vs CN, and an accuracy of 92% for classifying
MCIc vs CN. The results exhibit that the proposed algorithm outruns other
state-of-the-art methods for the multi-class classification and classification
between MCIc and MCInc.
Creator
Ambily Francis, Immanuel Alex Pandian
Source
http://telkomnika.uad.ac.id
Date
Nov 26, 2022
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Ambily Francis, Immanuel Alex Pandian, “TELKOMNIKA Telecommunication Computing Electronics and Control
Ensemble learning approach for multi-class classification of Alzheimer’s stages using magnetic resonance imaging,” Repository Horizon University Indonesia, accessed February 5, 2025, https://repository.horizon.ac.id/items/show/4492.
Ensemble learning approach for multi-class classification of Alzheimer’s stages using magnetic resonance imaging,” Repository Horizon University Indonesia, accessed February 5, 2025, https://repository.horizon.ac.id/items/show/4492.