Leveraging Deep Learning for Enhanced Detection of Alzheimer’s Disease Through Morphometric Analysis of Brain Images

Basit Öğe Kaydı
dc.contributor.authorÇelebi, Selahattin Barış
dc.contributor.authorEmiroğlu, Bülent Gürsel
dc.date.accessioned2025-01-21T16:28:01Z
dc.date.available2025-01-21T16:28:01Z
dc.date.issued2023
dc.departmentKırıkkale Üniversitesi
dc.description.abstractThis study investigates the efficacy of tensor-based morphometry (TBM) in detecting Alzheimer’s Disease (AD) using deep learning techniques. The primary focus is on discerning the volumetric variations in brain tissues characteristic of AD, Mild Cognitive Impairment (MCI), and cognitively normal (CN) conditions. TBM, as a measure of minute local volume differences, is employed as the distinguishing feature. The results are juxtaposed with those obtained from machine-learning-based methods, trained using a variety of medical images. Three unique models were developed for this purpose. The first model, trained using medial slices of the brain (train: 1622; test: 406), displayed an accuracy of less than 50%. The second model utilized axial brain slices procured at 5-pixel intervals, encompassing the hippocampus and the temporal lobe (train: 1632; test: 406), and demonstrated a significantly improved accuracy of 93%. The third model, fine-tuned with small kernel sizes to better extract localized changes from the image data used in the second model, achieved an accuracy of 92%. The findings suggest that the application of TBM and deep learning to medial slices alone is insufficient for an accurate diagnosis of AD. However, employing TBM with deep learning techniques to slices covering the hippocampus and temporal lobe can potentially offer a highly accurate approach for early AD detection. Notably, the use of small filters to extract detailed features from TBM did not enhance the model's performance. This research underscores the potential of deep learning in advancing the field of AD detection and diagnosis, providing crucial insights into the future development of diagnostic tools. © 2023 Lavoisier. All rights reserved.
dc.description.sponsorshipDOD ADNI; 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; University of Southern California, USC; Alzheimer's Disease Neuroimaging Initiative, ADNI; Northern California Institute for Research and Education, NCIRE
dc.identifier.doi10.18280/ts.400405
dc.identifier.endpage1365
dc.identifier.issn0765-0019
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85173282376
dc.identifier.scopusqualityN/A
dc.identifier.startpage1355
dc.identifier.urihttps://doi.org/10.18280/ts.400405
dc.identifier.urihttps://hdl.handle.net/20.500.12587/23450
dc.identifier.volume40
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInternational Information and Engineering Technology Association
dc.relation.ispartofTraitement du Signal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectAlzheimer’s Disease; convolutional neural networks; deep learning; image classification; tensor-based morphometry
dc.titleLeveraging Deep Learning for Enhanced Detection of Alzheimer’s Disease Through Morphometric Analysis of Brain Images
dc.typeArticle

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu