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...
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| Published in: | Frontiers in physiology Vol. 15; p. 1380459 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Frontiers Media S.A
09.07.2024
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| ISSN: | 1664-042X, 1664-042X |
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| 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. |
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| AbstractList | 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.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. 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. 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. 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. 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. 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. 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. |
| Author | Falkowski-Gilski, Przemysław Ramesh, G. P. Ganesan, Praveena Falkowska-Gilska, Bożena |
| AuthorAffiliation | 1 Department of Electronics and Communication Engineering , St. Peter’s Institute of Higher Education and Research , Chennai , India 2 Faculty of Electronics , Telecommunications and Informatics , Gdansk University of Technology , Gdansk , Poland 3 Specialist Diabetes Outpatient Clinic , Olsztyn , Poland |
| AuthorAffiliation_xml | – name: 2 Faculty of Electronics , Telecommunications and Informatics , Gdansk University of Technology , Gdansk , Poland – name: 3 Specialist Diabetes Outpatient Clinic , Olsztyn , Poland – name: 1 Department of Electronics and Communication Engineering , St. Peter’s Institute of Higher Education and Research , Chennai , India |
| Author_xml | – sequence: 1 givenname: Praveena surname: Ganesan fullname: Ganesan, Praveena – sequence: 2 givenname: G. P. surname: Ramesh fullname: Ramesh, G. P. – sequence: 3 givenname: Przemysław surname: Falkowski-Gilski fullname: Falkowski-Gilski, Przemysław – sequence: 4 givenname: Bożena surname: Falkowska-Gilska fullname: Falkowska-Gilska, Bożena |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39045216$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.neuroscience.2019.05.014 10.1016/j.neucom.2018.11.111 10.1109/ACCESS.2019.2926288 10.1155/2017/1952373 10.1016/j.neuroimage.2020.117460 10.1109/ACCESS.2022.3164734 10.1016/j.compmedimag.2018.09.009 10.1016/j.cmpb.2021.106032 10.1016/j.mehy.2020.109696 10.4218/etrij.10.1510.0132 10.1109/JTEHM.2023.3285723 10.1002/ima.22967 10.1007/s11042-020-10331-8 10.1109/ACCESS.2021.3072336 10.1016/j.neuroscience.2021.01.002 10.1016/j.inffus.2018.10.009 10.1109/TNNLS.2021.3118369 10.3390/diagnostics12071531 10.1016/j.jneumeth.2019.01.011 10.1016/j.chemolab.2020.104054 10.1016/j.compbiomed.2021.104678 10.1109/TMI.2021.3063150 10.1016/j.measurement.2020.108838 10.1016/j.measurement.2017.09.052 10.1109/AVSS.2010.9 10.1007/s11042-021-10688-4 10.1016/j.irbm.2020.06.006 10.1016/j.isatra.2019.07.001 10.1109/TPAMI.2018.2889096 10.1016/j.engappai.2020.103541 10.1007/s00521-021-05983-y 10.1007/s00530-021-00797-3 10.1038/s41598-020-74399-w 10.1109/TMI.2021.3077079 10.3390/make5020031 10.3390/math11122633 10.3390/app13137833 10.1016/j.nicl.2018.101645 10.3390/s22082911 10.1016/j.bspc.2023.105407 10.3390/brainsci13060893 10.1016/j.dsp.2016.08.003 10.12785/ijcds/120102 10.3390/math10081259 10.1016/j.compbiomed.2020.103764 10.1016/j.asoc.2019.105510 10.1007/s00521-021-06816-8 10.1016/j.eswa.2021.115123 |
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| Copyright | Copyright © 2024 Ganesan, Ramesh, Falkowski-Gilski and Falkowska-Gilska. Copyright © 2024 Ganesan, Ramesh, Falkowski-Gilski and Falkowska-Gilska. 2024 Ganesan, Ramesh, Falkowski-Gilski and Falkowska-Gilska |
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| Keywords | Otsu thresholding deep belief network tunicate swarm algorithm magnetic resonance imaging classification accuracy Alzheimer’s disease detection |
| Language | English |
| License | Copyright © 2024 Ganesan, Ramesh, Falkowski-Gilski and Falkowska-Gilska. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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Alzheimer’s Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is... Alzheimer's Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is necessary to reduce... Introduction: Alzheimer's Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is... Introduction: Alzheimer’s Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is... |
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| SubjectTerms | Alzheimer’s disease detection classification accuracy deep belief network magnetic resonance imaging Otsu thresholding Physiology tunicate swarm algorithm |
| Title | Detection of Alzheimer’s disease using Otsu thresholding with tunicate swarm algorithm and deep belief network |
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