Jiang Jingwan, Kang Li, Huang Jianjun, Zhang Tijiang
College of Information Engineering, Shenzhen University, Shenzhen 518060, China.
College of Information Engineering, Shenzhen University, Shenzhen 518060, China.
Neurosci Lett. 2020 Jun 21;730:134971. doi: 10.1016/j.neulet.2020.134971. Epub 2020 May 4.
Mild cognitive impairment (MCI) is an early sign of Alzheimer's disease (AD) which is the fourth leading disease mostly found in the aged population. Early intervention of MCI will possibly delay the progress towards AD, and this makes it very important to diagnose early MCI (EMCI). However, it is very difficult since the subtle difference between EMCI and cognitively normal control (NC). For improving classification performance, this paper presents a deep learning based diagnosis approach using structure MRI images for exploiting deeply embedded diagnosis features; then a feature selection strategy is performed to eliminate redundant features. A Support Vector Machine (SVM) is further employed to distinguish EMCI from NC. Experiments were performed on the publicly available ADNI dataset with a total of 120 subjects. The classification results demonstrate the superior performance of the proposed method with accuracy of 89.4% for EMCI versus NC.
轻度认知障碍（MCI）是阿尔茨海默病（AD）的早期迹象，AD是在老年人群中发现的第四大主要疾病。对MCI进行早期干预可能会延缓向AD的进展，这使得早期诊断早期MCI（EMCI）非常重要。然而，这非常困难，因为EMCI与认知正常对照（NC）之间存在细微差异。为了提高分类性能，本文提出了一种基于深度学习的诊断方法，使用结构MRI图像来挖掘深度嵌入的诊断特征；然后执行特征选择策略以消除冗余特征。进一步采用支持向量机（SVM）来区分EMCI和NC。在总共120名受试者的公开可用ADNI数据集上进行了实验。分类结果表明，所提出方法具有卓越性能，EMCI与NC分类的准确率为89.4%。
