Search
Detection of Alzheimer's disease using Otsu thresholding with tunicate swarm algorithm and deep belief network
Abstract
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.
Citations
-
0
CrossRef
-
0
Web of Science
-
0
Scopus
Authors (4)
-
Praveena Ganesan
-
G. P. Ramesh
-
Bożena Falkowska-Gilska
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3389/fphys.2024.1380459
- License
-
open in new tab
Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Frontiers in Physiology
no. 15,
ISSN: 1664-042X - Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Ganesan P., Ramesh G. P., Falkowski-Gilski P., Falkowska-Gilska B.: Detection of Alzheimer's disease using Otsu thresholding with tunicate swarm algorithm and deep belief network// Frontiers in Physiology -Vol. 15, (2024), s.138045-
- DOI:
- Digital Object Identifier (open in new tab) 10.3389/fphys.2024.1380459
- Sources of funding:
-
- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
seen 31 times
Recommended for you
Tool Wear Monitoring Using Improved Dragonfly Optimization Algorithm and Deep Belief Network
- L. Gertrude David,
- R. Kumar Patra,
- P. Falkowski-Gilski
- + 2 authors
Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
- M. A. Jopek,
- K. Pastuszak,
- S. Cygert
- + 5 authors
Intracranial hemorrhage detection in 3D computed tomography images using a bi-directional long short-term memory network-based modified genetic algorithm
- J. Sengupta,
- R. Alzbutas,
- P. Falkowski-Gilski
- + 1 authors