Multi-scale Attention Network for Early Detection of Alzheimer’s Disease from MRI images

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-scale Attention Network for Early Detection of Alzheimer’s Disease from MRI images

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Multi-scale Attention Network for Early Detection of Alzheimer’s Disease from MRI images

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/VJ02010019" target="_blank" >VJ02010019: Nástroje forenzní expertizy ručně psaného písma</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

979-8-3503-9328-6

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

50-55

Název nakladatele

IEEE

Místo vydání

Ghent, Belgium

Místo konání akce

Gent, Belgium

Datum konání akce

30. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—