MRCAD: A Prediction Algorithm for Alzheimer's Disease in Structural MRI Based on the Correlation of Multi-Brain-Region ROI Features

The computer-aided early diagnosis method of Alzheimer's disease (AD) based on structural magnetic resonance imaging (sMRI) has effectively improved the screening efficiency. However, current sMRI-based auxiliary diagnosis is mostly limited to using global information for diagnosis. There are s...

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Vydáno v:IEEE International Conference on Signal and Image Processing Applications (Online) s. 172 - 177
Hlavní autoři: Song, Siyi, Meng, Qingguo, Han, Xianjun, Xu, Chenchu, Wang, Huabing, Li, Xuejun
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 12.07.2025
Témata:
Accuracy
Adaptation models
Alzheimer's disease
Attention Fusion
Brain modeling
Correlation
Cross-brain-region Weighting
Feature extraction
Lesions
Magnetic resonance imaging
Predictive models
sMRI
ISSN:2642-6471
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Shrnutí:The computer-aided early diagnosis method of Alzheimer's disease (AD) based on structural magnetic resonance imaging (sMRI) has effectively improved the screening efficiency. However, current sMRI-based auxiliary diagnosis is mostly limited to using global information for diagnosis. There are still challenges in using the degree of structural variation in different brain regions and the correlation between brain regions to assist in disease diagnosis. To address these challenges, we propose the MRCAD model based on the interconnection of features from ROIs in various brain regions. This model consists of three parts. Firstly, the multi-brain-region feature extraction module: Extract 90 ROI brain partitions of the brain through a standard brain template, and use a network with shared parameters to accurately extract lesion features. Secondly, the cross-brainregion feature weighting module: Use the brain-region feature correlation method to adaptively enhance the feature weights with a higher degree of correlation, so that the network can pay attention to the correlation between brain regions and adaptively weight according to the contribution of each brain region. Thirdly, the cross-brain-region attention fusion module: Use the attention mechanism to combine clinical scores with the weighted brain-region features to enhance the accuracy of the network. The experimental results of our proposed MRCAD method on the ADNI database show that the prediction accuracy of the MRCAD model for AD, MCI, and CN reaches 95%. Compared with other 3D structural MRI-based AD prediction models, it has a higher prediction accuracy.
ISSN:2642-6471
DOI:10.1109/ICSIP65915.2025.11171568