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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • e-ISSN

  • 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