Alzheimer's disease pre-diagnosis method based on 3D convolutional neural network

A convolutional neural network and pre-diagnosis technology, applied in diagnosis, diagnostic recording/measurement, medical science, etc., can solve the problem of losing useful information and achieve the effects of preventing over-fitting, high accuracy, and getting rid of physical exertion

Active Publication Date: 2021-04-20
SICHUAN UNIV
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AI Technical Summary

Problems solved by technology

However, human-designed low-level features often lose useful information prematurely

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  • Alzheimer's disease pre-diagnosis method based on 3D convolutional neural network
  • Alzheimer's disease pre-diagnosis method based on 3D convolutional neural network
  • Alzheimer's disease pre-diagnosis method based on 3D convolutional neural network

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[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0055] The present invention provides a technical solution: a method for prediagnosing Alzheimer's disease based on a 3D convolutional neural network, comprising the following steps:

[0056] S1: Select data set: use ADNI database, select MRI and DTI image data;

[0057] The data used in the present invention are all from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). Through the investigation of the existing data in the ADNI ...

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Abstract

The invention discloses an Alzheimer's disease pre-diagnosis method based on a 3D convolutional neural network. The method comprises the following steps: S1, selecting a data set: selecting MRI and DTI image data through adopting an ADNI database; S2, performing data preprocessing: preprocessing the selected MRI and DTI image data; S3, adopting a DL method based on the CNN comprising the specific steps: performing 3D convolution; adopting a batch regularization technology, and adopting a linear rectification function as an activation function; and performing 3D pooling; S4, establishing a network system structure; and S5, realizing performance evaluation, adding DTI data to improve diagnosis accuracy, and acquiring the DTI image data by scanning the same parameter from the same system as an MRI image. Therefore, the method also has the characteristics of low cost, no trauma and easiness in obtaining, and is easy to popularize in clinical application.

Description

technical field [0001] The invention relates to the technical field of disease prediction, in particular to a method for prediagnosing Alzheimer's disease based on a 3D convolutional neural network. Background technique [0002] Alzheimer's disease (AD) is an irreversible progressive neurodegenerative disease with progressive brain cell death and brain volume loss (Ewers et al., 2011). It is estimated that approximately 75% of dementia patients worldwide are of Alzheimer's disease (Holtzman et al., 2011), and more than 30 million people are affected worldwide (Barnes and Yaffe, 2011). The Delphi Concordance Study predicts that the number of AD patients will increase to 42.3 million by 2020 and 81.1 million by 2040 (Ferri et al., 2005). The treatment of Alzheimer's disease is quite difficult, and there is currently no clear and effective treatment. Mild cognitive impairment (MCI) is an intermediate state between normal controls (NC) and AD, often divided into early MCI (EMC...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/00
Inventor 袁榕澳郭延芝王聪刘一静胡际帆
Owner SICHUAN UNIV
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