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Enhancing multi-stage classification of alzheimer's disease with attention mechanism

Multi-stage classification of Alzheimer's disease (AD) refers to classifying the disease into its multiple stages. Aside from a binary classification task that classifies between the normal control (NC) and AD stages only, an additional prodromal stage known as Mild Cognitive Impairment (MCI) i...

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Main Authors: Wong, Pui Ching, Abdullah, Shahrum Shah, Shapiai, Mohd. Ibrahim
Format: Conference or Workshop Item
Published: 2023
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/107788/
http://dx.doi.org/10.1109/IICAIET59451.2023.10291792
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spelling my.utm.1077882024-10-08T06:06:28Z http://eprints.utm.my/107788/ Enhancing multi-stage classification of alzheimer's disease with attention mechanism Wong, Pui Ching Abdullah, Shahrum Shah Shapiai, Mohd. Ibrahim T Technology (General) Multi-stage classification of Alzheimer's disease (AD) refers to classifying the disease into its multiple stages. Aside from a binary classification task that classifies between the normal control (NC) and AD stages only, an additional prodromal stage known as Mild Cognitive Impairment (MCI) is also being classified. MCI is the stage between the healthy subjects known as the NC class, and the patients with heavy symptoms, in the AD class. In other words, MCI subjects only have slight or mild symptoms of Alzheimer's, thus leading it to be a challenge for detection. Classification models usually perform well in binary classification tasks, but not in multistage classification tasks, due to the faint difference in their features. Thus, this research proposes the incorporation of an attention mechanism into the classification model to increase its multi-stage classification performance. The attention mechanism facilitates the classification task by identifying the important features in MRI images so that the model can better differentiate the multiple classes. The MRI data used in this study is obtained from the Open Access Series of Imaging Studies (OASIS) database. The experimental results show that the attention-incorporated model has achieved an improved classification performance as compared to the normal model without attention. The generalizability of the enhanced model is also improved as observed from the training-classification gap results. Hence, the exceptional performance of the attention mechanism positions it as a solution to boost and enhance multi-stage AD classification. 2023 Conference or Workshop Item PeerReviewed Wong, Pui Ching and Abdullah, Shahrum Shah and Shapiai, Mohd. Ibrahim (2023) Enhancing multi-stage classification of alzheimer's disease with attention mechanism. In: 2023 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 12 September 2023-14 September 2023, Kota Kinabalu, Sabah, Malaysia. http://dx.doi.org/10.1109/IICAIET59451.2023.10291792
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic T Technology (General)
spellingShingle T Technology (General)
Wong, Pui Ching
Abdullah, Shahrum Shah
Shapiai, Mohd. Ibrahim
Enhancing multi-stage classification of alzheimer's disease with attention mechanism
description Multi-stage classification of Alzheimer's disease (AD) refers to classifying the disease into its multiple stages. Aside from a binary classification task that classifies between the normal control (NC) and AD stages only, an additional prodromal stage known as Mild Cognitive Impairment (MCI) is also being classified. MCI is the stage between the healthy subjects known as the NC class, and the patients with heavy symptoms, in the AD class. In other words, MCI subjects only have slight or mild symptoms of Alzheimer's, thus leading it to be a challenge for detection. Classification models usually perform well in binary classification tasks, but not in multistage classification tasks, due to the faint difference in their features. Thus, this research proposes the incorporation of an attention mechanism into the classification model to increase its multi-stage classification performance. The attention mechanism facilitates the classification task by identifying the important features in MRI images so that the model can better differentiate the multiple classes. The MRI data used in this study is obtained from the Open Access Series of Imaging Studies (OASIS) database. The experimental results show that the attention-incorporated model has achieved an improved classification performance as compared to the normal model without attention. The generalizability of the enhanced model is also improved as observed from the training-classification gap results. Hence, the exceptional performance of the attention mechanism positions it as a solution to boost and enhance multi-stage AD classification.
format Conference or Workshop Item
author Wong, Pui Ching
Abdullah, Shahrum Shah
Shapiai, Mohd. Ibrahim
author_facet Wong, Pui Ching
Abdullah, Shahrum Shah
Shapiai, Mohd. Ibrahim
author_sort Wong, Pui Ching
title Enhancing multi-stage classification of alzheimer's disease with attention mechanism
title_short Enhancing multi-stage classification of alzheimer's disease with attention mechanism
title_full Enhancing multi-stage classification of alzheimer's disease with attention mechanism
title_fullStr Enhancing multi-stage classification of alzheimer's disease with attention mechanism
title_full_unstemmed Enhancing multi-stage classification of alzheimer's disease with attention mechanism
title_sort enhancing multi-stage classification of alzheimer's disease with attention mechanism
publishDate 2023
url http://eprints.utm.my/107788/
http://dx.doi.org/10.1109/IICAIET59451.2023.10291792
_version_ 1814043522906456064
score 13.211869

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