SVM (Support Vector Machine) based Alzheimer's disease characteristic classification method and system

Abstract

The invention discloses an SVM based Alzheimer's disease characteristic classification method and system. The method comprises that magnetic resonance imaging data of the Alzheimer's disease is obtained; an improved genetic algorithm is used to carry out characteristic optimization searching on the obtained magnetic resonance imaging data to obtain key characteristics of the Alzheimer's disease; and an SVM classifier is used to classify data to be classified according to the extracted key characteristics, and a classification result of the Alzheimer's disease is obtained. The system comprisesa data obtaining module, a characteristic optimization searching module and a classification module. A device comprises a memory and a processor. The improved genetic algorithm takes the average classification accuracy as the fitness value to improve the feature extraction efficiency of the Alzheimer's disease, the SVM classifier ensures the classification performance of the Alzheimer's disease, and the method and system are visual and easy to realize, can be generalized effectively, and has a good identification performance. The method and device can be widely applied to the field of computeraided diagnosis.

Description

technical field [0001] The invention relates to the field of computer-aided diagnosis, in particular to a method and system for classifying Alzheimer's disease features based on a support vector machine. 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, MCI and ...

AI Technical Summary

Problems solved by technology

[0006] However, there is no report on the combination of genetic algorithm and support vector machine for feature extraction and classification of Alzheimer's disease, and the efficiency and classification effect of feature extraction for Alzheimer's disease need to be further improved

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