Alzheimer's disease classification method, system and device based on Gaussian process classification

Abstract

The invention discloses an Alzheimer's disease classification method, an Alzheimer's disease classification system and an Alzheimer's disease classification device based on Gaussian process classification. The Alzheimer's disease classification method comprises the steps of: acquiring magnetic resonance imaging data of Alzheimer's diseases; extracting key features used for Alzheimer's disease classification from the acquired magnetic resonance imaging data by adopting a key feature extraction algorithm based on a total correlation coefficient; and classifying data to be classified by adoptinga Gaussian process classifier according to the extracted key features to obtain a classification result of the Alzheimer's diseases. The Alzheimer's disease classification system comprises a data acquisition module, a feature extraction module, and a classification module. The Alzheimer's disease classification device comprises a memory and a processor. The Alzheimer's disease classification method, the Alzheimer's disease classification system and the Alzheimer's disease classification device improve the feature extraction efficiency of the Alzheimer's diseases by adopting the key feature extraction algorithm based on the total correlation coefficient, ensure the classification performance of the Alzheimer's diseases by means of the Gaussian process classifier, are easy to implement, havebetter nonlinear processing performance, and can be widely applied to the field of computer aided diagnosis.

Description

technical field [0001] The invention relates to the field of computer-aided diagnosis, in particular to a classification method, system and device for Alzheimer's disease based on Gaussian process classification. Background technique [0002] Alzheimer's disease (Alzheimer's Disease, AD) is an irreversible chronic neurodegenerative disease and a persistent high-level neurological dysfunction. The existing drug treatments for AD are very limited, but early and accurate detection and treatment can slow down the disease process. Mild cognitive impairment (Mild Cognitive Impairment, MCI) is a transitional stage between normal healthy people (Health Controllers, HC) and AD, and MCI patients are a high-risk population for AD. Studies at home and abroad have pointed out that the important pathological signs and biomarkers of AD can be measured by Magnetic Resonance Imaging (MRI). The method of extracting effective features from MRI to classify and identify the three stages of AD,...

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Problems solved by technology

[0006] However, there is no report on the combination of overall correlation coefficient and Gaussian process classification for feature extraction and classification of Alzheimer's disease. The efficiency and classification effect of feature extraction for Alzheimer's disease need to be further improved

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