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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
Published on: August 7, 2017
MHAGuideNet: a 3D pre-trained guidance model for Alzheimer's Disease diagnosis using 2D multi-planar sMRI images
Yuanbi Nie1, Qiushi Cui2, Wenyuan Li1
1School of Electrical Engineering, Chongqing University, Shapingba, Chongqing, 400044, China.
Background:
Alzheimer's Disease is a neurodegenerative condition leading to irreversible and progressive brain damage, with possible features such as structural atrophy. Effective precision diagnosis is crucial for slowing disease progression and reducing the incidence rate and morbidity. Traditional computer-aided diagnostic methods using structural MRI data often focus on capturing such features but face challenges, like overfitting with 3D image analysis and insufficient feature capture with 2D slices, potentially missing multi-planar information, and the complementary nature of features across different orientations.
Methods:
The study introduces MHAGuideNet, a classification method incorporating a guidance network utilizing multi-head attention. The model utilizes a pre-trained 3D convolutional neural network to direct the feature extraction of multi-planar 2D slices, specifically targeting the detection of features like structural atrophy. Additionally, a hybrid 2D slice-level network combining 2D CNN and 2D Swin Transformer is employed to capture the interrelations between the atrophy in different brain structures associated with Alzheimer's Disease.
Results:
The proposed MHAGuideNet is tested using two datasets: the ADNI and OASIS datasets. The model achieves an accuracy of 97.58%, specificity of 99.89%, F1 score of 93.98%, and AUC of 99.31% on the ADNI test dataset, demonstrating superior performance in distinguishing between Alzheimer's Disease and cognitively normal subjects. Furthermore, testing on the independent OASIA test dataset yields an accuracy of 96.02%, demonstrating the model's robust performance across different datasets.
Conclusion:
MHAGuideNet shows great promise as an effective tool for the computer-aided diagnosis of Alzheimer's Disease. Within the guidance of information from the 3D pre-trained CNN, the ability to leverage multi-planar information and capture subtle brain changes, including the interrelations between different structural atrophies, underscores its potential for clinical application.
