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...
Uloženo v:
| Vydáno v: | Frontiers in physiology Ročník 15; s. 1380459 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Switzerland
Frontiers Media S.A
09.07.2024
|
| Témata: | |
| ISSN: | 1664-042X, 1664-042X |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| 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. |
|---|---|
| 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 |
| BookMark | eNp9ks1u1DAUhSNUREvpC7BAXrKZwT-JE69QVShUqtQNSOwsx7meuCR2sJ2OyorX6OvxJDidoWpZ4I2t63O-e6V7XhYHzjsoitcErxlrxDsz9bdxTTEt14Q1uKzEs-KIcF6ucEm_HTx6HxYnMV7jfEpMMSYvikMmsoESflRMHyCBTtY75A06HX72YEcIv3_dRdTZCCoCmqN1G3SV4oxSHyD2fuiWytamHqXZWa0SoLhVYURq2PiQ6_nlOtQBTKiFwYJBDtLWh--viudGDRFO9vdx8fX845ezz6vLq08XZ6eXK11ykVaVabHmoq46XUHVUU5Inr2ujRLUQK2EbgwXLWsqggUTxkAlmG404xyD6hg7Li523M6razkFO6pwK72y8r7gw0aqkKweQOqqhXbh85KXmDUtNoSYlmlVa0HbJrPe71jT3I7QaXApqOEJ9OmPs73c-BuZoZwRvhDe7gnB_5ghJjnaqGEYlAM_R8lwkztTLGiWvnnc7KHL351lAd0JdPAxBjAPEoLlkg15nw25ZEPus5FNzT8mbZNa9p4HtsP_rH8AS-TDIQ |
| CitedBy_id | crossref_primary_10_1002_ima_70180 crossref_primary_10_1007_s11831_025_10228_5 crossref_primary_10_1186_s12911_025_03073_w crossref_primary_10_1109_ACCESS_2025_3529914 |
| 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 |
| ContentType | Journal Article |
| 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 |
| Copyright_xml | – notice: Copyright © 2024 Ganesan, Ramesh, Falkowski-Gilski and Falkowska-Gilska. – notice: Copyright © 2024 Ganesan, Ramesh, Falkowski-Gilski and Falkowska-Gilska. 2024 Ganesan, Ramesh, Falkowski-Gilski and Falkowska-Gilska |
| DBID | AAYXX CITATION NPM 7X8 5PM DOA |
| DOI | 10.3389/fphys.2024.1380459 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic CrossRef PubMed |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Anatomy & Physiology |
| DocumentTitleAlternate | Ganesan et al |
| EISSN | 1664-042X |
| ExternalDocumentID | oai_doaj_org_article_c5beb11206464038b0f11fb3ca7c92b8 PMC11263168 39045216 10_3389_fphys_2024_1380459 |
| Genre | Journal Article |
| GroupedDBID | 53G 5VS 9T4 AAFWJ AAKDD AAYXX ACGFO ACGFS ADBBV ADRAZ AENEX AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS BCNDV CITATION DIK EMOBN F5P GROUPED_DOAJ GX1 HYE KQ8 M48 M~E O5R O5S OK1 PGMZT RNS RPM ACXDI IPNFZ NPM RIG 7X8 5PM |
| ID | FETCH-LOGICAL-c469t-5fb0c6975dc5e5d261120077fa92fe7a9c8f69b38510939ffe593c8c3660ead33 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 5 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001274030400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1664-042X |
| IngestDate | Fri Oct 03 12:53:09 EDT 2025 Tue Sep 30 17:08:20 EDT 2025 Thu Oct 02 06:41:42 EDT 2025 Mon Jul 21 05:33:41 EDT 2025 Tue Nov 18 22:16:59 EST 2025 Sat Nov 29 03:00:01 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| 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. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c469t-5fb0c6975dc5e5d261120077fa92fe7a9c8f69b38510939ffe593c8c3660ead33 |
| Notes | 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 |
| OpenAccessLink | https://doaj.org/article/c5beb11206464038b0f11fb3ca7c92b8 |
| PMID | 39045216 |
| PQID | 3084032092 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_c5beb11206464038b0f11fb3ca7c92b8 pubmedcentral_primary_oai_pubmedcentral_nih_gov_11263168 proquest_miscellaneous_3084032092 pubmed_primary_39045216 crossref_primary_10_3389_fphys_2024_1380459 crossref_citationtrail_10_3389_fphys_2024_1380459 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-07-09 |
| PublicationDateYYYYMMDD | 2024-07-09 |
| PublicationDate_xml | – month: 07 year: 2024 text: 2024-07-09 day: 09 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland |
| PublicationTitle | Frontiers in physiology |
| PublicationTitleAlternate | Front Physiol |
| PublicationYear | 2024 |
| Publisher | Frontiers Media S.A |
| Publisher_xml | – name: Frontiers Media S.A |
| References | Qu (B34) 2023; 11 Shukla (B40); 34 Shukla (B38) 2023; 5 Kabir (B21) 2010 Ebrahimi-Ghahnavieh (B10) 2019 Sathiyamoorthi (B37) 2021; 171 Ghazi (B13) 2021; 225 Goh (B15) 2018; 114 Alhassan (B1) 2022; 10 Shukla (B39); 87 Yue (B49) 2019; 7 Vaithinathan (B43) 2019; 318 Lian (B27) 2020; 42 AlSaeed (B3) 2022; 22 Fan (B11) 2021; 33 Tuncer (B42) 2020; 203 Zhang (B50) 2019; 414 Yu (B48) 2022; 34 Mehmood (B29) 2021; 460 Kabir (B22) 2012; 9 Basaia (B6) 2019; 21 Hassan (B16) 2019; 51 Sabar (B36) 2020; 97 Wang (B45) 2022; 34 JabidKabir (B19) 2010; 32 Chui (B9) 2022; 12 Kaur (B25) 2020; 90 Li (B26) 2018; 70 Naz (B30) 2022; 28 Liu (B28) 2021; 203 Tan (B41) 2021; 80 Zhu (B51) 2021; 40 Arabali (B4) 2022; 10 Ning (B31) 2021; 40 Rani Kaka (B35) 2022; 12 Kang (B23) 2021; 136 Alqahtani (B2) 2023; 13 Chelladurai (B8) 2023; 13 Xiao (B47) 2017; 2017 Venugopalan (B44) 2021; 11 Hu (B18) 2020 Houssein (B17) 2021; 9 Ghosh (B14) 2023; 11 Ashraf (B5) 2021; 80 Bi (B7) 2020; 392 Kaplan (B24) 2020; 139 Feng (B12) 2017; 60 Puente-Castro (B33) 2020; 120 Wang (B46) 2020; 96 Janghel (B20) 2021; 42 Pan (B32) 2021; 180 |
| References_xml | – volume: 414 start-page: 273 year: 2019 ident: B50 article-title: Voxel-based morphometry: improving the diagnosis of Alzheimer’s disease based on an extreme learning machine method from the ADNI cohort publication-title: Neuroscience doi: 10.1016/j.neuroscience.2019.05.014 – volume: 392 start-page: 296 year: 2020 ident: B7 article-title: Computer aided Alzheimer’s disease diagnosis by an unsupervised deep learning technology publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.11.111 – volume: 7 start-page: 93752 year: 2019 ident: B49 article-title: Hierarchical feature extraction for early Alzheimer’s disease diagnosis publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2926288 – volume: 2017 start-page: 1952373 year: 2017 ident: B47 article-title: Brain MR image classification for Alzheimer’s disease diagnosis based on multifeature fusion publication-title: Comput. Math. Methods Med. doi: 10.1155/2017/1952373 – volume: 225 start-page: 117460 year: 2021 ident: B13 article-title: Robust parametric modeling of Alzheimer’s disease progression publication-title: NeuroImage doi: 10.1016/j.neuroimage.2020.117460 – volume: 10 start-page: 39204 year: 2022 ident: B4 article-title: An adaptive tunicate swarm algorithm for optimization of shallow foundation publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3164734 – start-page: 133 year: 2019 ident: B10 article-title: Transfer learning for Alzheimer’s disease detection on MRI images – volume: 70 start-page: 101 year: 2018 ident: B26 article-title: Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks publication-title: Comput. Med. Imaging Graph. doi: 10.1016/j.compmedimag.2018.09.009 – volume: 203 start-page: 106032 year: 2021 ident: B28 article-title: Alzheimer’s disease detection using depthwise separable convolutional neural networks publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2021.106032 – volume: 139 start-page: 109696 year: 2020 ident: B24 article-title: Brain tumor classification using modified local binary patterns (LBP) feature extraction methods publication-title: Med. Hypotheses doi: 10.1016/j.mehy.2020.109696 – volume: 32 start-page: 784 year: 2010 ident: B19 article-title: Robust facial expression recognition based on local directional pattern publication-title: ETRI J. doi: 10.4218/etrij.10.1510.0132 – volume: 11 start-page: 405 year: 2023 ident: B34 article-title: A graph convolutional network based on univariate neurodegeneration biomarker for Alzheimer’s disease diagnosis publication-title: IEEE J. Transl. Eng. Health Med. doi: 10.1109/JTEHM.2023.3285723 – volume: 34 start-page: e22967 ident: B40 article-title: Structural biomarker-based Alzheimer's disease detection via ensemble learning techniques publication-title: Int. J. Imaging Syst. Technol. doi: 10.1002/ima.22967 – volume: 80 start-page: 30117 year: 2021 ident: B5 article-title: Deep transfer learning for alzheimer neurological disorder detection publication-title: Multimedia Tools Appl. doi: 10.1007/s11042-020-10331-8 – volume: 9 start-page: 56066 year: 2021 ident: B17 article-title: An improved tunicate swarm algorithm for global optimization and image segmentation publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3072336 – volume: 460 start-page: 43 year: 2021 ident: B29 article-title: A transfer learning approach for early diagnosis of Alzheimer’s disease on MRI images publication-title: Neuroscience doi: 10.1016/j.neuroscience.2021.01.002 – volume: 51 start-page: 10 year: 2019 ident: B16 article-title: Human emotion recognition using deep belief network architecture publication-title: Inf. Fusion doi: 10.1016/j.inffus.2018.10.009 – volume: 34 start-page: 4401 year: 2022 ident: B48 article-title: Morphological feature visualization of Alzheimer’s disease via multidirectional perception GAN publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2021.3118369 – volume: 12 start-page: 1531 year: 2022 ident: B9 article-title: An MRI scans-based Alzheimer’s disease detection via convolutional neural network and transfer learning publication-title: Diagnostics doi: 10.3390/diagnostics12071531 – start-page: 526 year: 2010 ident: B21 article-title: A local directional pattern variance (LDPv) based face descriptor for human facial expression recognition – volume: 318 start-page: 84 year: 2019 ident: B43 article-title: A novel texture extraction technique with T1 weighted MRI for the classification of Alzheimer’s disease publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2019.01.011 – volume: 203 start-page: 104054 year: 2020 ident: B42 article-title: An automated residual exemplar local binary pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image publication-title: Chemom. Intelligent Laboratory Syst. doi: 10.1016/j.chemolab.2020.104054 – volume: 136 start-page: 104678 year: 2021 ident: B23 article-title: Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer’s disease diagnosis publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2021.104678 – volume: 40 start-page: 1632 year: 2021 ident: B31 article-title: Relation-induced multi-modal shared representation learning for Alzheimer’s disease diagnosis publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2021.3063150 – volume: 171 start-page: 108838 year: 2021 ident: B37 article-title: A deep convolutional neural network based computer aided diagnosis system for the prediction of Alzheimer’s disease in MRI images publication-title: Measurement doi: 10.1016/j.measurement.2020.108838 – volume: 114 start-page: 298 year: 2018 ident: B15 article-title: Performance analysis of image thresholding: Otsu technique publication-title: Measurement doi: 10.1016/j.measurement.2017.09.052 – volume: 9 start-page: 382 year: 2012 ident: B22 article-title: Local directional pattern variance (LDPV): a robust feature descriptor for facial expression recognition publication-title: Int. Arab J. Inf. Technol. doi: 10.1109/AVSS.2010.9 – volume: 80 start-page: 21499 year: 2021 ident: B41 article-title: Performance analysis of Otsu thresholding for sign language segmentation publication-title: Multimedia Tools Appl. doi: 10.1007/s11042-021-10688-4 – volume: 42 start-page: 258 year: 2021 ident: B20 article-title: Deep convolution neural network based system for early diagnosis of Alzheimer’s disease publication-title: IRBM doi: 10.1016/j.irbm.2020.06.006 – volume: 96 start-page: 457 year: 2020 ident: B46 article-title: A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network publication-title: ISA Trans. doi: 10.1016/j.isatra.2019.07.001 – volume: 42 start-page: 880 year: 2020 ident: B27 article-title: Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI publication-title: IEEE Trans. Pattern Analysis Mach. Intell. doi: 10.1109/TPAMI.2018.2889096 – volume: 90 start-page: 103541 year: 2020 ident: B25 article-title: Tunicate swarm algorithm: a new bio-inspired based metaheuristic paradigm for global optimization publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2020.103541 – volume: 33 start-page: 13587 year: 2021 ident: B11 article-title: U-Net based analysis of MRI for Alzheimer’s disease diagnosis publication-title: Neural Comput. Appl. doi: 10.1007/s00521-021-05983-y – volume: 28 start-page: 85 year: 2022 ident: B30 article-title: Transfer learning using freeze features for alzheimer neurological disorder detection using ADNI dataset publication-title: Multimed. Syst. doi: 10.1007/s00530-021-00797-3 – volume: 11 start-page: 3254 year: 2021 ident: B44 article-title: Multimodal deep learning models for early detection of Alzheimer’s disease stage publication-title: Sci. Rep. doi: 10.1038/s41598-020-74399-w – volume: 40 start-page: 2354 year: 2021 ident: B51 article-title: Dual attention multi-instance deep learning for Alzheimer's disease diagnosis with structural MRI publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2021.3077079 – volume: 5 start-page: 512 year: 2023 ident: B38 article-title: Alzheimer’s disease detection from fused PET and MRI modalities using an ensemble classifier publication-title: Mach. Learn. Knowl. Extr. doi: 10.3390/make5020031 – volume: 11 start-page: 2633 year: 2023 ident: B14 article-title: A robust distributed deep learning approach to detect Alzheimer’s disease from MRI images publication-title: Mathematics doi: 10.3390/math11122633 – volume: 13 start-page: 7833 year: 2023 ident: B2 article-title: Deep belief networks (DBN) with IoT-based Alzheimer’s disease detection and classification publication-title: Appl. Sci. doi: 10.3390/app13137833 – volume: 21 start-page: 101645 year: 2019 ident: B6 article-title: Automated classification of Alzheimer’s disease and Mild cognitive impairment using a single MRI and deep neural networks publication-title: NeuroImage Clin. doi: 10.1016/j.nicl.2018.101645 – volume: 22 start-page: 2911 year: 2022 ident: B3 article-title: Brain MRI analysis for Alzheimer’s disease diagnosis using CNN-based feature extraction and machine learning publication-title: Sensors doi: 10.3390/s22082911 – volume: 87 start-page: 105407 ident: B39 article-title: Analyzing subcortical structures in Alzheimer's disease using ensemble learning publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2023.105407 – volume: 13 start-page: 893 year: 2023 ident: B8 article-title: fMRI-based Alzheimer’s disease detection using the SAS method with multi-layer perceptron network publication-title: Brain Sci. doi: 10.3390/brainsci13060893 – volume: 60 start-page: 186 year: 2017 ident: B12 article-title: A multi-scale 3D Otsu thresholding algorithm for medical image segmentation publication-title: Digit. Signal Process. doi: 10.1016/j.dsp.2016.08.003 – volume: 12 start-page: 9 year: 2022 ident: B35 article-title: Alzheimer’s disease detection using correlation based ensemble feature selection and multi support vector machine publication-title: Int. J. Comput. Digital Syst. doi: 10.12785/ijcds/120102 – volume: 10 start-page: 1259 year: 2022 ident: B1 article-title: Enhanced fuzzy elephant herding optimization-based OTSU segmentation and deep learning for Alzheimer’s disease diagnosis publication-title: Mathematics doi: 10.3390/math10081259 – start-page: 1323 year: 2020 ident: B18 article-title: Medical image reconstruction using generative adversarial network for alzheimer disease assessment with class-imbalance problem – volume: 120 start-page: 103764 year: 2020 ident: B33 article-title: Automatic assessment of Alzheimer’s disease diagnosis based on deep learning techniques publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2020.103764 – volume: 97 start-page: 105510 year: 2020 ident: B36 article-title: An evolutionary hyper-heuristic to optimise deep belief networks for image reconstruction publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.105510 – volume: 34 start-page: 8657 year: 2022 ident: B45 article-title: Brain stroke lesion segmentation using consistent perception generative adversarial network publication-title: Neural Comput. Appl. doi: 10.1007/s00521-021-06816-8 – volume: 180 start-page: 115123 year: 2021 ident: B32 article-title: Adaptive center pixel selection strategy in local binary pattern for texture classification publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.115123 |
| SSID | ssj0000402001 |
| Score | 2.3981438 |
| Snippet | Introduction:
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... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 1380459 |
| 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 |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/39045216 https://www.proquest.com/docview/3084032092 https://pubmed.ncbi.nlm.nih.gov/PMC11263168 https://doaj.org/article/c5beb11206464038b0f11fb3ca7c92b8 |
| Volume | 15 |
| WOSCitedRecordID | wos001274030400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1664-042X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000402001 issn: 1664-042X databaseCode: DOA dateStart: 20100101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1664-042X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000402001 issn: 1664-042X databaseCode: M~E dateStart: 20100101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELag4sAFAeURHpWREBcUasdxbB8XaMUBCgdAe7PiV7vSbnaVZKnKAfE3-Hv8EsZOutpFCC5coshxkpFnbM83M55B6KmTxIuy5LmpBMlLV7vcFIHlBqaSKgsjffKef34rTk7kdKo-bJX6ijFhQ3rgYeAOLTewnNACts6qJEwaEigNhtlaWAXfiqsvaD1bYCqtwREWETqckgEUpg5DtBQAHizKF5RJUGTUzk6UEvb_Scv8PVhya_c5volujGojngzk3kJXfHMb7U8agMyLC_wMp0DOZCHfR6vXvk8BVg1eBjyZfz3zs4Vvf37_0eHRH4NjuPspft93a9wDN7vRCYWjWRb3w4kRj7vzul3gen66bKEd7hqHnfcrbDyorgE3Qwz5HfTp-Ojjqzf5WFght4CG-5wHQ2ylBHeWe-4ARNFoshShVkXwolZWhkoZBtoYUUyF4LliVlpWVQQkj7G7aK9ZNv4-wkVhJXG0Ep6H0hBeS1Fza7xz1oFuQjNELwdZ2zHreCx-MdeAPiJjdGKMjozRI2My9HzzzmrIufHX3i8j7zY9Y77s1ABSpEcp0v-Sogw9ueS8hvkVnSZ145frTjMCEJgVRBUZujdIwuZXTMWE9LTKkNyRkR1adp80s7OUwzue3Io1wx78D-ofoutxRFIUsXqE9vp27R-ja_ZLP-vaA3RVTOVBmh9wffft6Bfn4hmM |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Detection+of+Alzheimer%27s+disease+using+Otsu+thresholding+with+tunicate+swarm+algorithm+and+deep+belief+network&rft.jtitle=Frontiers+in+physiology&rft.au=Ganesan%2C+Praveena&rft.au=Ramesh%2C+G+P&rft.au=Falkowski-Gilski%2C+Przemys%C5%82aw&rft.au=Falkowska-Gilska%2C+Bo%C5%BCena&rft.date=2024-07-09&rft.issn=1664-042X&rft.eissn=1664-042X&rft.volume=15&rft.spage=1380459&rft_id=info:doi/10.3389%2Ffphys.2024.1380459&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1664-042X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1664-042X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1664-042X&client=summon |