An Adaptive Feature Fusion Network for Alzheimer’s Disease Prediction
Paper
Paper/Presentation Title | An Adaptive Feature Fusion Network for Alzheimer’s Disease Prediction |
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Presentation Type | Paper |
Authors | Wei, Shicheng, Li, Yan and Yang, Wencheng |
Journal or Proceedings Title | Proceedings of the 12th International Conference on Health Information Science (HIS 2023) |
Journal Citation | 14305, pp. 271-282 |
Number of Pages | 12 |
Year | 2023 |
Place of Publication | Germany |
ISBN | 9789819971077 |
9789819971084 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-99-7108-4 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-981-99-7108-4_23 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-981-99-7108-4 |
Conference/Event | 12th International Conference on Health Information Science (HIS 2023) |
Event Details | 12th International Conference on Health Information Science (HIS 2023) Parent International Conference on Health Information Science (HIS) Delivery In person Event Date 23 to end of 24 Oct 2023 Event Location Melbourne, Australia |
Abstract | Structural Magnetic Resonance Imaging (sMRI) of brain structures has proven effective in predicting early lesions associated with Alzheimer’s disease (AD). However, identifying the AD lesion area solely through sMRI is challenging, as only a few abnormal texture areas are directly linked to the lesion. Moreover, the observed lesion area varies when examining two-dimensional MRI slides in different directions within three-dimensional space. Traditional convolutional neural networks struggle to accurately focus on the AD lesion structure. To address this issue, we propose an adaptive feature fusion network for AD prediction. Firstly, an adaptive feature fusion module is constructed to enhance attention towards lesion areas by considering features from three directions and fusing them together. Secondly, a multi-channel group convolution module is designed to improve the network’s ability to extract fine-grained features by separating convolutional channels. Finally, a regularized loss function is introduced to combine the SoftMax loss function and clustering loss function. This helps enhance the network’s ability to differentiate between different sample types. Experimental results from binary classification tests on the dataset obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed method accurately distinguishes between normal control (NC), progressive mild cognitive impairment (pMCI), stable mild cognitive impairment (sMCI), and AD. |
Keywords | Alzheimer’s disease; convolutional neural network; Deep Learning; sMRI |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Series | Lecture Notes in Computer Science |
Byline Affiliations | School of Mathematics, Physics and Computing |
https://research.usq.edu.au/item/z3034/an-adaptive-feature-fusion-network-for-alzheimer-s-disease-prediction
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