Alzheimer's disease classification system based on deep learning

A deep learning and classification system technology, applied in the field of Alzheimer's disease classification system, can solve the problems of functional connection as static, huge functional connection information, and reduce classification reliability, etc., to achieve rapid and effective clinical diagnosis, easy training, The effect of auxiliary clinical diagnosis

Pending Publication Date: 2022-04-19
SHANDONG NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] (1) The temporal characteristics of functional connectivity contain a wealth of information and functional connectivity can express changes in the temporal pattern of neural activity in resting or task states. It is unreasonable to regard the functional connectivity of the entire scanning phase as static
[0008] (2) The functional connection information between different regions of interest (ROI) or different brain regions is too large, and redundant features inevitably exist, thereby reducing the reliability of classification
[0009] (3) Most of the commonly used deep learning networks are used to extract features such as image textures, and are not sensitive to fMRI with rich temporal information.

Method used

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  • Alzheimer's disease classification system based on deep learning
  • Alzheimer's disease classification system based on deep learning
  • Alzheimer's disease classification system based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0038] Such as figure 1 As shown, a deep learning-based classification system for Alzheimer's disease, including:

[0039] The data acquisition module is configured to acquire image data;

[0040] The independent decomposition module is configured to use an independent component analysis algorithm to obtain independent components according to the acquired image data;

[0041] The classification module is configured to use the long short memory network model to obtain the classification result of Alzheimer's disease according to the independent components;

[0042] Among them, independent component analysis algorithm was used to obtain group-level independent components, and reverse reconstruction regression was used to obtain independent components of each independent individual.

[0043] Specifically, a deep learning-based classification method for Alzheimer's disease is implemented, including:

[0044] Step 1. Preprocessing the original image to preserve the valuable info...

Embodiment 2

[0077] A computer-readable storage medium, in which a plurality of instructions are stored, and the instructions are suitable for being loaded by a processor of a terminal device and executing the deep learning-based Alzheimer's disease classification system provided in Embodiment 1.

Embodiment 3

[0079] A terminal device, including a processor and a computer-readable storage medium, the processor is used to implement instructions; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded and executed by the processor provided in Embodiment 1 A deep learning-based classification system for Alzheimer's disease.

[0080] Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code emb...

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Abstract

The invention provides an Alzheimer's disease classification system based on deep learning, and the system comprises a data obtaining module which is configured to obtain image data; the independent decomposition module is configured to obtain independent components by utilizing an independent component analysis algorithm according to the acquired image data; the classification module is configured to obtain an Alzheimer's disease classification result by using a long short term memory network model according to the independent components; wherein group-level independent components are obtained by utilizing an independent component analysis algorithm, and the independent component of each independent individual is obtained by adopting reverse reconstruction regression. According to the method, the Alzheimer's disease is diagnosed based on independent component analysis and the long and short memory network, and compared with a traditional method based on a region of interest, the requirement for priori knowledge can be lowered; compared with a voxel-based method, the method has the advantages that the risk of overfitting is avoided, the dimensionality of the features is reduced, and the model is easier to train.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence and medical image processing, in particular to a deep learning-based Alzheimer's disease classification system. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] The principle of functional magnetic resonance imaging (fMRI) is to use magnetic resonance imaging to measure the changes in hemodynamics caused by neuronal activity, which can give a more precise functional relationship between different regions of the brain. In the early stages of some diseases, there may be no structural or clinical symptoms, but degeneration will occur in some functional aspects. Compared with other brain imaging techniques, fMRI has the advantages of high resolution and less damage. Therefore, fMRI has become an indispensable part of auxiliary diagnosis of b...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/00A61B5/055
CPCA61B5/4088A61B5/7264A61B5/7267A61B5/055A61B5/0033A61B5/0042
Inventor 乔建苹王荣刘洪嘉
Owner SHANDONG NORMAL UNIV
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