Multi-scale Attention Network for Early Detection of Alzheimer’s Disease from MRI images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU149871" target="_blank" >RIV/00216305:26220/23:PU149871 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10333096" target="_blank" >https://ieeexplore.ieee.org/document/10333096</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICUMT61075.2023.10333096" target="_blank" >10.1109/ICUMT61075.2023.10333096</a>
Alternative languages
Result language
angličtina
Original language name
Multi-scale Attention Network for Early Detection of Alzheimer’s Disease from MRI images
Original language description
Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that affects brain cells and causes irreversible memory loss, often known as dementia. Many individuals die from this disease each year due to its incurable nature. However, the timely identification of the ailment can play a pivotal role in mitigating its progression. Nowadays, deep learning is used to design an automated system that can detect and classify AD in the early stages. Thus, a novel multi-scale attention network (MSAN-Net) is introduced in this study. The proposed technique uses brain magnetic resonance imaging (MRI) to categorize images into four stages; non-demented, mild demented, very mild demented, and moderate demented. The proposed work is compared with four state-of-the-art methods, and the experimental results suggest that the MSAN-Net exhibits superior performance than the compared approaches.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/VJ02010019" target="_blank" >VJ02010019: Tools for Handwriting fORensics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)
ISBN
979-8-3503-9328-6
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
50-55
Publisher name
IEEE
Place of publication
Ghent, Belgium
Event location
Gent, Belgium
Event date
Oct 30, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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