Abstract
Alzheimer disease (AD) is mainly manifested as insidious onset, chronic progressive cognitive decline and non-cognitive neuropsychiatric symptoms, which seriously affects the quality of life of the elderly and causes a very large burden on society and families. This paper uses graph theory to analyze the constructed brain network, and extracts the node degree, node efficiency, and node betweenness centrality parameters of the two modal brain networks. The T test method is used to analyze the difference of graph theory parameters between normal people and AD patients, and brain regions with significant differences in graph theory parameters are selected as brain network features. By analyzing the calculation principles of the conventional convolutional layer and the depth separable convolution unit, the computational complexity of them is compared. The depth separable convolution unit decomposes the traditional convolution process into spatial convolution for feature extraction and point convolution for feature combination, which greatly reduces the number of multiplication and addition operations in the convolution process, while still being able to obtain comparisons. Aiming at the special convolution structure of the depth separable convolution unit, this paper proposes a channel pruning method based on the convolution structure and explains its pruning process. Multimodal neuroimaging can provide complete information for the quantification of Alzheimer’s disease. This paper proposes a cascaded three-dimensional neural network framework based on single-modal and multi-modal images, using MRI and PET images to distinguish AD and MCI from normal samples. Multiple three-dimensional CNN networks are used to extract recognizable information in local image blocks. The high-level two-dimensional CNN network fuses multi-modal features and selects the features of discriminative regions to perform quantitative predictions on samples. The algorithm proposed in this paper can automatically extract and fuse the features of multi-modality and multi-regions layer by layer, and the visual analysis results show that the abnormally changed regions affected by Alzheimer’s disease provide important information for clinical quantification.
Highlights
Alzheimer disease (AD) is a neurodegenerative disease in the brain
The existing quantification methods of AD, Mild Cognitive Impairment (MCI), and HC based on deep learning have generally achieved high accuracy in the quantification of AD group vs. HC group, but the accuracy rate is slightly less in AD group vs. MCI group and MCI group vs. HC group
The network formed by the connection of white matter fibers between all brain regions of the whole brain is called DTI Structural Connectivity Network (DTISCN)
Summary
Alzheimer disease (AD) is a neurodegenerative disease in the brain. It is one of the most common types of dementia, accounting for about 60–80% of the total number of dementia patients (Lee et al, 2019; Spasov et al, 2019). Deep learning can perform image analysis and intelligent quantification of diseases, and improve the efficiency of medical data collection and processing, thereby improving the accuracy of doctors in diagnosis and treatment of diseases, so that patients can get more timely, more complete, and more accurate treatment (Chen et al, 2019). This paper uses graph theory to extract brain network features and verify the effectiveness of the features. Graph theory parameters with obvious differences between normal people and AD patients are taken as brain network characteristics.
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