Brain disease classification system based on self-attention mechanism

Abstract

The invention relates to the technical field of image processing, and proposes a brain disease classification system based on a self-attention mechanism, aiming at solving the technical problems of low classification accuracy caused by the complicated process of preprocessing, feature extraction and feature selection of magnetic resonance images required in the classification and diagnosis of brain diseases. For this purpose, the brain disease classification system based on the self-attention mechanism in the invention comprises the following steps: pre-processing the acquired human brain magnetic resonance images of the brain disease patients to obtain the gray matter density map of the human brain; using a pre-constructed brain disease classification model to classify the gray matter density map to obtain a brain disease category of the brain disease patient, wherein The pre-constructed brain disease classification model is a three-dimensional convolutional neural network model basedon the self-attention mechanism. The system shown in the embodiment of the invention can quickly and accurately classify the categories of brain diseases.

Description

technical field [0001] The present invention relates to the technical field of medical image processing, in particular to the field of image processing based on deep learning, and in particular to a brain disease classification system based on a self-attention mechanism. Background technique [0002] Alzheimer's disease (Alzheimer's Disease, AD), as a main type of senile dementia, seriously threatens the health of the elderly. The cognitive ability of Alzheimer's disease patients is gradually impaired, and the ability to live is gradually lost, which brings a great burden to the society and family members. Therefore, early detection and diagnosis of Alzheimer's disease patients is very important. [0003] The rapid development of non-invasive neuroimaging technology has greatly facilitated the study of normal and abnormal brain structure and brain function. Magnetic Resonance Imaging (MRI), as a high spatial resolution, non-invasive medical imaging technique, has been wide...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the above-mentioned technical problems in the prior art, that is, in order to solve the complex process of preprocessing, feature extraction and feature selection in the traditional brain disease classification and the technical problem of low classification accuracy caused by it, for this Purpose, the present invention provides a kind of brain disease classification system based on self-attention mechanism, to solve the above problems

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