Detail publikace

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

AGGARWAL, V. SAHU, G. DUTTA, M. JONÁK, M. BURGET, R.

Originální název

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

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Alzheimer’s disease, deep learning, MRI, multi-class classification, attention network

Autoři

AGGARWAL, V.; SAHU, G.; DUTTA, M.; JONÁK, M.; BURGET, R.

Vydáno

30. 10. 2023

Nakladatel

IEEE

Místo

Ghent, Belgium

ISBN

979-8-3503-9328-6

Kniha

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

Strany od

50

Strany do

55

Strany počet

6

URL

https://ieeexplore.ieee.org/document/10333096

BibTex

@inproceedings{BUT185771,
  author="Vaishali {Aggarwal} and Geet {Sahu} and Malay Kishore {Dutta} and Martin {Jonák} and Radim {Burget}",
  title="Multi-scale Attention Network for Early Detection of Alzheimer’s Disease from MRI images",
  booktitle="2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)",
  year="2023",
  pages="50--55",
  publisher="IEEE",
  address="Ghent, Belgium",
  doi="10.1109/ICUMT61075.2023.10333096",
  isbn="979-8-3503-9328-6",
  url="https://ieeexplore.ieee.org/document/10333096"
}