Advancing early diagnosis of Alzheimer's disease with next-generation deep learning methods

Basit Öğe Kaydı
dc.authoridOZDEMIR, Cuneyt/0000-0002-9252-5888
dc.authoridDogan, Yahya/0000-0003-1529-6118
dc.contributor.authorOzdemir, Cuneyt
dc.contributor.authorDogan, Yahya
dc.date.accessioned2024-12-24T19:25:23Z
dc.date.available2024-12-24T19:25:23Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractAlzheimer's disease, characterized by cognitive decline and memory impairment, poses a significant healthcare challenge. This study presents a specially designed CNN model, utilizing contemporary approaches, to distinguish between various types of Alzheimer's disease. This model can serve as an early diagnostic tool to prevent the disease from progressing towards more pronounced and severe dementia symptoms. In this context, the performance of various transfer learning models has been examined, leading to the development of a specialized model integrating compression and excitation blocks, an innovative Avg-TopK pooling layer, and the SMOTE technique to handle data imbalance. The ablation study results demonstrate the critical role of these components, highlighting the model's effectiveness and innovative design. This study is novel in that it combines modern methodologies for detecting Alzheimer's disease, resulting in a model with state-of-the-art accuracy of 99.84% and improved computing efficiency. Grad-CAM analysis further demonstrates that the model focuses on cortical areas during classification, underscoring its potential as a robust diagnostic tool. These innovations represent a significant advancement over existing models, positioning this study as a pioneering effort in the early diagnosis of Alzheimer's disease. This study aims to contribute significantly to both academic research and medical applications by focusing on integrating artificial intelligence methodologies into medical diagnosis.
dc.description.sponsorshipIn this study, the expertise and experience of expert radiologists Dr. Mehmet Ali GEDIK and Dr. Sahinde ATLANOGLU in interpreting Grad-Cam heatmaps to assess the accuracy of the model's focused areas have greatly contributed to enhancing the quality, reliability, and validity of this research. We would like to express our sincere gratitude to them for their valuable contributions to this study.
dc.identifier.doi10.1016/j.bspc.2024.106614
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85197510559
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2024.106614
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6388
dc.identifier.volume96
dc.identifier.wosWOS:001267623900001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofBiomedical Signal Processing and Control
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectAlzheimer's disease
dc.subjectDeep learning
dc.subjectSMOTE
dc.subjectSqueeze and excitation block
dc.subjectAvg-TopK
dc.titleAdvancing early diagnosis of Alzheimer's disease with next-generation deep learning methods
dc.typeArticle

Dosyalar

Koleksiyon

WOS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu