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

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dc.contributor.authorÇelebi, Selahattin Barış
dc.contributor.authorEmiroğlu, Bülent Gürsel
dc.date.accessioned2025-01-21T16:26:41Z
dc.date.available2025-01-21T16:26:41Z
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. © 2023 by the authors.
dc.description.sponsorshipDOD ADNI; EuroImmun; National Institutes of Health, NIH, (U01 AG024904); National Institutes of Health, NIH; U.S. Department of Defense, DOD, (W81XWH-12-2-0012); U.S. Department of Defense, DOD; National Institute on Aging, NIA; National Institute of Biomedical Imaging and Bioengineering, NIBIB; Eli Lilly and Company; F. Hoffmann-La Roche; Alzheimer's Disease Neuroimaging Initiative, ADNI
dc.identifier.doi10.3390/app13158686
dc.identifier.issn2076-3417
dc.identifier.issue15
dc.identifier.scopus2-s2.0-85167901131
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app13158686
dc.identifier.urihttps://hdl.handle.net/20.500.12587/23175
dc.identifier.volume13
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofApplied Sciences (Switzerland)
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectAlzheimer’s disease; deep learning; image classification; tensor-based morphometry; transfer learning
dc.titleA Novel Deep Dense Block-Based Model for Detecting Alzheimer’s Disease
dc.typeArticle

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