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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" }