Hybrid CNN-SVM for Alzheimers Disease Classification from Structural MRI and the Alzheimers Disease Neuroimaging Initiative(ADNI)

摘要：

　　Alzheimers disease(AD)is a progressive neurological disorder among the elders,which results in memory-related issues in subjects.An accurate classification of patients with AD and mild cognitive impairment(MCI)from healthy control subjects(HC)based on structural magnetic resonance imaging(MRI)is of critical clinical importance.In this paper,good intermediate representations of MRI are obtained from a pre-trained convolutional neural network(CNN).Principal component analysis(PCA)and sequential feature selection(SFS)are applied for feature selection,while a support vector machine(SVM)is adopted to evaluate the classification accuracy.422 Alzheimers Disease Neuroimaging Initiative(ADNI)baseline MRI were used for development and validation of our proposed method.As a result,this paper achieved a classification accuracy of 90%for binary classification of AD and HC,81%for AD and MCI and 72%for MCI and HC.

关键词： Alzheimers disease classification transfer learning

作者: Lan Lin Baiwen Zhang Shuicai Wu

作者单位: College of Life Science and Bioengineering,Beijing University of Technology,Beijing,100124,China

会议类型: 国际会议

会议名称: 2018 International Conference on Biomedical Engineering, Machinery and Earth Science (BEMES 2018)(2018年生物医学工程、机械与地球科学国际会议)

会议地点: 广州

会议语种:英文

页码: 199-203

在线出版日期: 2018-09-01（万方平台首次上网日期，不代表论文的发表时间）