Publication:
Deep learning techniques for automated dementia diagnosis using neuroimaging modalities: a systematic review

Placeholder

School / College / Institute

Organizational Unit

Program

KU-Authors

KU Authors

Co-Authors

Katar, Oguzhan
Ak, Murat
Al-Antari, Mugahed A.
Yasan Ak, Nehir
Yildirim, Ozal
Mir, Hasan S.
Tan, Ru-San
Rajendra Acharya, U.

Publication Date

Language

Embargo Status

Journal Title

Journal ISSN

Volume Title

Alternative Title

Abstract

Dementia is a condition that often comes with aging and affects how people think, remember, and behave. Diagnosing dementia early is important because it can greatly improve patients' lives. This systematic review looks at how deep learning (DL) techniques have been used to diagnose dementia automatically from 2012 to 2023. We explore how different DL methods like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Deep Neural Networks (DNN) are used to diagnose types of dementia such as Alzheimer's, vascular dementia, and Lewy body dementia. We also discuss the difficulties of using DL for diagnosing dementia, like the lack of large and varied datasets and the challenge of applying models to different groups of people. These issues indicate the need for more dependable and understandable models that consider a wide range of patient characteristics and biomarkers. Longitudinal studies are also needed to understand how the disease progresses and how treatments work. Collaboration among researchers, doctors, and data scientists is crucial to ensure DL models are scientifically sound and effective in clinical settings. In summary, DL techniques show promise for automated dementia diagnosis and could improve how accurately and efficiently it is diagnosed in practice. However, further research is needed to address the challenges highlighted in this review.

Source

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Computer science

Citation

Has Part

Source

IEEE ACCESS

Book Series Title

Edition

DOI

10.1109/ACCESS.2024.3454709

item.page.datauri

Link

Rights

Copyrights Note

Endorsement

Review

Supplemented By

Referenced By

0

Views

0

Downloads

View PlumX Details