现代电子技术2024，Vol.47Issue(11)：99-104,6.DOI:10.16652/j.issn.1004-373x.2024.11.017
基于低秩学习的阿尔茨海默病诊断方法
Alzheimer's disease diagnosis method based on low-rank learning
摘要
Abstract
Alzheimer's disease is the most common one of senile dementias,which currently affects a significant number of elderly individuals both in China and around the world.However,Alzheimer's disease is still characterized by low diagnosis rate,high rate of misdiagnosis,and limited treatment efficacy in China.Therefore,there is an urgent need to explore early diagnostic solutions for Alzheimer's disease to detect potential patients promptly and provide intervention and treatment.At present,computer-aided diagnosis(CAD)is very popular,enabling healthcare professionals to efficiently and rapidly diagnose diseases.Hence,an Alzheimer's disease diagnostic method based on low-rank learning(LRL)is proposed.In this method,MRI image data is utilized to extract multiple template features and fused them.To address the challenge of the limited samples and the high feature dimension in MRI data,the LRL approach is employed for feature selection to obtain the most representative feature subset.Subsequently,the selected features are input into a support vector machine(SVM)classifier for both three-class and four-class classification tasks.Experimental results demonstrate that the proposed LRL model outperforms the other classical feature selection methods.On both primary evaluation index accuracy(ACC)and F1 score,the LRL model achieves 74.94%and 75.80%in the three-class classification task,and 63.88%and 59.99%in the four-class classification task,respectively.关键词
阿尔茨海默病/MRI图像/低秩学习/支持向量机/多分类/计算机辅助诊断Key words
Alzheimer's disease/MRI image/LRL/SVM/multi-classification/CAD分类
信息技术与安全科学引用本文复制引用
张军,李钰彬..基于低秩学习的阿尔茨海默病诊断方法[J].现代电子技术,2024,47(11):99-104,6.基金项目
国家自然科学基金资助项目(62162002) （62162002）
国家自然科学基金资助项目(61662002) （61662002）
江西省自然科学基金资助项目(20212BAB202002) （20212BAB202002）
