Brain image classification device based on self-adaptive receptive field 3D spatial attention

A technology of receptive field and attention, applied in image analysis, image enhancement, image data processing, etc., can solve problems that cannot meet the accuracy requirements of classification, and achieve improved classification effect, easy implementation, good robustness and accuracy Effect

Pending Publication Date: 2020-12-11
HANGZHOU NORMAL UNIVERSITY
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Problems solved by technology

Although the existing methods have a good effect on the classification of brain medical ...

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  • Brain image classification device based on self-adaptive receptive field 3D spatial attention
  • Brain image classification device based on self-adaptive receptive field 3D spatial attention

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Embodiment Construction

[0031] The present invention is further analyzed below in conjunction with specific embodiment.

[0032] A brain image classification device for Alzheimer's disease based on adaptive receptive field 3D spatial attention, including:

[0033] A data acquisition module, configured to acquire brain MRI images of T1 structural items;

[0034] The data preprocessing module is used to sequentially perform origin correction, gray matter segmentation, registration and modulation processing on the MRI images obtained by the data acquisition module;

[0035] The origin correction is to correct the origin of the MRI image to the position of the anterior commissure of the brain. Among them, the origin correction is a routine technical operation, so it will not be explained in detail.

[0036] The gray matter segmentation is to remove the skull region from the image processed by the origin correction, and then extract the gray matter region.

[0037] The registration is to register the g...

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Abstract

The invention discloses a brain image classification device based on self-adaptive receptive field 3D spatial attention. A 3D-ResNet18 network based on self-adaptive receptive field 3D spatial attention is constructed by introducing an attention mechanism, a 3D spatial attention module is composed of a plurality of branches, information of different scales on each branch can be fused, different branches are weighted in the fusion process, and neurons can self-adaptively adjust the size of the receptive field conveniently. The network is used for classifying brain MRI images of the Alzheimer'sdisease, so that the classification effect is improved. The method is easy to implement, data preprocessing operation is simple, and better robustness and accuracy rate are achieved.

Description

technical field [0001] The invention belongs to the technical field of network pattern recognition, and in particular relates to a brain image classification device for Alzheimer's disease based on adaptive receptive field 3D spatial attention. Background technique [0002] Alzheimer's disease is a progressive neurodegenerative disease and the most common form of dementia, which can lead to memory loss, decreased thinking ability, and even affect physical activity. Globally, with the increasing trend of global aging, the number of patients with Alzheimer's disease is gradually increasing. Therefore, the cost of Alzheimer's disease treatment is also increasing sharply, seriously affecting the quality of life of patients and their families and the development of society. The diagnosis of Alzheimer's disease is receiving increasing attention from researchers. [0003] Traditional machine learning methods are widely used in the research of medical images. So far, a variety of...

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06T7/33G06K9/62
CPCG06T7/0012G06T7/11G06T7/344G06T2207/10088G06T2207/30016G06F18/2414G06F18/25
Inventor 尉飞李秀梅葛青青孙军梅
Owner HANGZHOU NORMAL UNIVERSITY
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