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A robust and clinically applicable deep learning model for early detection of Alzheimer's.

Authors :
Rana, Md Masud
Islam, Md Manowarul
Talukder, Md. Alamin
Uddin, Md Ashraf
Aryal, Sunil
Alotaibi, Naif
Alyami, Salem A.
Hasan, Khondokar Fida
Moni, Mohammad Ali
Source :
IET Image Processing (Wiley-Blackwell); 12/11/2023, Vol. 17 Issue 14, p3959-3975, 17p
Publication Year :
2023

Abstract

Alzheimer's disease, often known as dementia, is a severe neurodegenerative disorder that causes irreversible memory loss by destroying brain cells. People die because there is no specific treatment for this disease. Alzheimer's is most common among seniors 65 years and older. However, the progress of this disease can be reduced if it can be diagnosed earlier. Recently, artificial intelligence has instilled hope in the diagnosis of Alzheimer's disease by performing sophisticated analyses on extensive patient datasets, enabling the identification of subtle patterns that may elude human experts. Researchers have investigated various deep learning and machine learning models to diagnose this disease at an early stage using image datasets. In this paper, a new Deep learning (DL) methodology is proposed, where MRI images are fed into the model after applying various pre‐processing techniques. The proposed Alzheimer's disease detection approach adopts transfer learning for multi‐class classification using brain MRIs. The MRI Images are classified into four categories: mild dementia (MD), moderate dementia (MOD), very mild dementia (VMD), and non‐dementia (ND). The model is implemented and extensive performance analysis is performed. The finding shows that the model obtains 97.31% accuracy. The model outperforms the state‐of‐the‐art models in terms of accuracy, precision, recall, and F‐score. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
17519659
Volume :
17
Issue :
14
Database :
Complementary Index
Journal :
IET Image Processing (Wiley-Blackwell)
Publication Type :
Academic Journal
Accession number :
174011280
Full Text :
https://doi.org/10.1049/ipr2.12910