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

A convolutional neural network, pre-judgment technology, applied in the field of disease prediction, can solve problems such as loss of useful information, and achieve the effect of preventing overfitting, high accuracy, and getting rid of physical exhaustion

Active Publication Date: 2022-03-22
SICHUAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

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

Method used

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

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Embodiment Construction

[0050] Next, the technical solutions in the embodiments of the present invention will be described in connection with the drawings of the embodiments of the present invention, and it is understood that the described embodiments are merely the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, those of ordinary skill in the art will belong to the scope of the present invention without all other embodiments obtained in the preparation of creative labor.

[0051] The present invention provides a technical solution: a pre-proportion method of Alzheimer's Alzheimer's premises based on 3D convolutional neural network, including the following steps:

[0052] S1: Select the data set: use the Adni database, select MRI and DTI image data;

[0053] The data used in the present invention is all from the Alzheimer's Nervine Commercial Image (Adni) Database (Adni.loni.usc.edu). By investigating existing data in the ADNI database,...

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Abstract

The invention discloses a pre-diagnosis method for Alzheimer's disease based on a 3D convolutional neural network, comprising the following steps: S1: selecting a data set: using ADNI database, selecting MRI and DTI image data; S2: data preprocessing: for all data Selected MRI and DTI image data for preprocessing; S3: CNN-based DL method, the specific steps include: 3D convolution; batch regularization technique using linear rectification function as activation function; 3D pooling, S4: building network architecture ;S5: Implementation and performance evaluation, the addition of DTI data can improve the accuracy of diagnosis, and the acquisition of DTI image data is the same as MRI images, obtained from the same parameter scan from the same system, so it is also low-cost, non-invasive, and easy to obtain It is easy to popularize in clinical application.

Description

Technical field [0001] The present invention relates to the field of disease prediction, and is specifically a pre-proprietary method of Alzheimeria based on 3D convolutional neural network. Background technique [0002] Alzheimer's disease (AD) is an irreversible conductive neurological degenerative disease, accompanied by performing brain cell death and brain volume (Ewers et al., 2011). It is estimated that approximately 75% of patients around the world belong to Alzhamimer (Holtzman et al.), With more than 30 million people around the world (Barnes and Yaffe, 2011). Delphi consistency research predicts that by 2020, the number of AD patients will increase to 42.3 million, and will increase to 811 million (Ferri et al., 2005). Alzheimer's disease is quite difficult, there is no clear and effective treatment. Mild cognitive dysfunction (MCI) is an intermediate state between normal control group (NC) and AD, often divided into early MCI (EMCI) and advanced MCI (LMCI). Over time,...

Claims

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Application Information

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Patent Type & Authority Patents(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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