Detection of Alzheimer’s disease using Otsu thresholding with tunicate swarm algorithm and deep belief network

Introduction: Alzheimer’s Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is necessary to reduce the mortality rate through slowing down its progression. The prevention and detection of AD is the emerging research topic for...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Frontiers in physiology Jg. 15; S. 1380459
Hauptverfasser: Ganesan, Praveena, Ramesh, G. P., Falkowski-Gilski, Przemysław, Falkowska-Gilska, Bożena
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media S.A 09.07.2024
Schlagworte:
Alzheimer’s disease detection
classification accuracy
deep belief network
magnetic resonance imaging
Otsu thresholding
Physiology
tunicate swarm algorithm
Otsu thresholding
deep belief network
tunicate swarm algorithm
magnetic resonance imaging
classification accuracy
Alzheimer’s disease detection
ISSN:1664-042X, 1664-042X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Introduction: Alzheimer’s Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is necessary to reduce the mortality rate through slowing down its progression. The prevention and detection of AD is the emerging research topic for many researchers. The structural Magnetic Resonance Imaging (sMRI) is an extensively used imaging technique in detection of AD, because it efficiently reflects the brain variations. Methods: Machine learning and deep learning models are widely applied on sMRI images for AD detection to accelerate the diagnosis process and to assist clinicians for timely treatment. In this article, an effective automated framework is implemented for early detection of AD. At first, the Region of Interest (RoI) is segmented from the acquired sMRI images by employing Otsu thresholding method with Tunicate Swarm Algorithm (TSA). The TSA finds the optimal segmentation threshold value for Otsu thresholding method. Then, the vectors are extracted from the RoI by applying Local Binary Pattern (LBP) and Local Directional Pattern variance (LDPv) descriptors. At last, the extracted vectors are passed to Deep Belief Networks (DBN) for image classification. Results and Discussion: The proposed framework achieves supreme classification accuracy of 99.80% and 99.92% on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL) datasets, which is higher than the conventional detection models.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Edited by: Carlos D. Maciel, University of São Paulo, Brazil
Reviewed by: Victor Hugo Batista Tsukahara, University of São Paulo, Brazil
Giovana Yuko Nakashima, Federal Institute of São Paulo, Brazil
ISSN:1664-042X
1664-042X
DOI:10.3389/fphys.2024.1380459