A Novel Deep Dense Block-Based Model for Detecting Alzheimer's Disease

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dc.authoridCELEBI, Selahattin Baris/0000-0002-6235-9348
dc.contributor.authorcelebi, Selahattin Baris
dc.contributor.authorEmiroglu, Buelent Gursel
dc.date.accessioned2025-01-21T16:35:11Z
dc.date.available2025-01-21T16:35:11Z
dc.date.issued2023
dc.departmentKırıkkale Üniversitesi
dc.description.abstractAlzheimer's disease (AD), the most common form of dementia and neurological disorder, affects a significant number of elderly people worldwide. The main objective of this study was to develop an effective method for quickly diagnosing healthy individuals (CN) before they progress to mild cognitive impairment (MCI). Moreover, this study presents a unique approach to decomposing AD into stages using machine-learning architectures with the help of tensor-based morphometric image analysis. The proposed model, which uses a neural network built on the Xception architecture, was thoroughly assessed by comparing it with the most recent convolutional neural network (CNN) models described in the literature. The proposed method outperformed the other models in terms of performance, achieving an impressive average classification accuracy of 95.81% using the dataset. It also had very high sensitivity, specificity, accuracy, and F1 scores, with average scores of 95.41%, 97.92%, 95.01%, and 95.21%, respectively. In addition, it showed a superior classification ability compared to alternative methods, especially for MCI estimation, as evidenced by a mean area under the ROC curve (AUC) of 0.97. Our study demonstrated the effectiveness of deep-learning-based morphometric analysis using brain images for early AD diagnosis.
dc.description.sponsorshipAlzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health); DOD ADNI (Department of Defense); National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; AbbVie; Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd.; Genentech, Inc.; Fujirebio [U01 AG024904]; GE Healthcare [W81XWH-12-2-0012]; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research amp; Development, LLC.; Johnson amp; Johnson Pharmaceutical Research amp; Development LLC.; Lumosity; Lundbeck; Merck amp; Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; Transition Therapeutics; Canadian Institutes of Health Research
dc.description.sponsorshipData collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research provides funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org, accessed on 6 July 2022). The grantee organization is the Northern California Institute for Research and Education, and this study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
dc.identifier.doi10.3390/app13158686
dc.identifier.issn2076-3417
dc.identifier.issue15
dc.identifier.urihttps://doi.org/10.3390/app13158686
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24093
dc.identifier.volume13
dc.identifier.wosWOS:001046129200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectAlzheimer's disease; transfer learning; image classification; deep learning; tensor-based morphometry
dc.titleA Novel Deep Dense Block-Based Model for Detecting Alzheimer's Disease
dc.typeArticle

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