A HybridOpt approach for early Alzheimer's Disease diagnostics with Ant Lion Optimizer (ALO).
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| Title: | A HybridOpt approach for early Alzheimer's Disease diagnostics with Ant Lion Optimizer (ALO). |
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| Authors: | A., Sasithradevi1 (AUTHOR) sasithradevi.a@vit.ac.in, Baskar, Chanthini2 (AUTHOR) chanthini.b@vit.ac.in, Shoba, S.1 (AUTHOR) shoba.s@vit.ac.in |
| Source: | Alexandria Engineering Journal. Dec2024, Vol. 109, p112-125. 14p. |
| Subjects: | Ant lions, Alzheimer's disease, Optimization algorithms, Blended learning, Feature selection |
| Abstract: | Alzheimer disease is a neurological disorder that affects the elderly, caused by abnormal protein buildup in the brain. It leads to difficulties such as financial mismanagement, disorientation, behavioral changes, and repetitive speech. The existing methods use traditional features to detect the early signs of AD with low detection accuracy. The potential features have to be identified that represent best the patterns associated with alzheimers. Feature selection using ant lion optimization resolves the issue by using complementary information from hybrid features. The proposed HybridOpt pipeling for AD diagnosis combines the high level and low level features for early stage detection The objective of this work is to select efficient features from different deep networks, such as AlexNet, Googlenet, VGG16, ResNet, Efficient, DenseNet, and traditional texture features. Ant Lion Optimization is used to select the best feature among the deep network and traditional texture feature groups. Extensive experimentation on two highly challenging datasets called the Alzheimer's disease neuroimage dataset and KAGGLE reveals that the proposed HybridOpt pipeline achieves an accuracy of 99% and 98.1% respectively. • Developing a HybridOpt based on Ant Lion Optimizer (ALO) for Alzheimer Disease (AD) diagnosis. • The comprehensive experiments has been conducted on ADNI and KAGGLE Dataset. • The HybridOpt system is analysed through comparison and ablation studies. • The proposed work is validated using Precision (P), Recall (R), F1 score (F1) and Accuracy (A). [ABSTRACT FROM AUTHOR] |
| Copyright of Alexandria Engineering Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 181775404 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A HybridOpt approach for early Alzheimer's Disease diagnostics with Ant Lion Optimizer (ALO). – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22A%2E%2C+Sasithradevi%22">A., Sasithradevi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sasithradevi.a@vit.ac.in</i><br /><searchLink fieldCode="AR" term="%22Baskar%2C+Chanthini%22">Baskar, Chanthini</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> chanthini.b@vit.ac.in</i><br /><searchLink fieldCode="AR" term="%22Shoba%2C+S%2E%22">Shoba, S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> shoba.s@vit.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Alexandria+Engineering+Journal%22">Alexandria Engineering Journal</searchLink>. Dec2024, Vol. 109, p112-125. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Ant+lions%22">Ant lions</searchLink><br /><searchLink fieldCode="DE" term="%22Alzheimer's+disease%22">Alzheimer's disease</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Blended+learning%22">Blended learning</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Alzheimer disease is a neurological disorder that affects the elderly, caused by abnormal protein buildup in the brain. It leads to difficulties such as financial mismanagement, disorientation, behavioral changes, and repetitive speech. The existing methods use traditional features to detect the early signs of AD with low detection accuracy. The potential features have to be identified that represent best the patterns associated with alzheimers. Feature selection using ant lion optimization resolves the issue by using complementary information from hybrid features. The proposed HybridOpt pipeling for AD diagnosis combines the high level and low level features for early stage detection The objective of this work is to select efficient features from different deep networks, such as AlexNet, Googlenet, VGG16, ResNet, Efficient, DenseNet, and traditional texture features. Ant Lion Optimization is used to select the best feature among the deep network and traditional texture feature groups. Extensive experimentation on two highly challenging datasets called the Alzheimer's disease neuroimage dataset and KAGGLE reveals that the proposed HybridOpt pipeline achieves an accuracy of 99% and 98.1% respectively. • Developing a HybridOpt based on Ant Lion Optimizer (ALO) for Alzheimer Disease (AD) diagnosis. • The comprehensive experiments has been conducted on ADNI and KAGGLE Dataset. • The HybridOpt system is analysed through comparison and ablation studies. • The proposed work is validated using Precision (P), Recall (R), F1 score (F1) and Accuracy (A). [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Alexandria Engineering Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.aej.2024.08.089 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 112 Subjects: – SubjectFull: Ant lions Type: general – SubjectFull: Alzheimer's disease Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Blended learning Type: general – SubjectFull: Feature selection Type: general Titles: – TitleFull: A HybridOpt approach for early Alzheimer's Disease diagnostics with Ant Lion Optimizer (ALO). Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: A., Sasithradevi – PersonEntity: Name: NameFull: Baskar, Chanthini – PersonEntity: Name: NameFull: Shoba, S. IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 12 Text: Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 11100168 Numbering: – Type: volume Value: 109 Titles: – TitleFull: Alexandria Engineering Journal Type: main |
| ResultId | 1 |
