Automated Detection of Alzheimer's Disease Using Brain MRI Images- A Study with Various Feature Extraction Techniques
Automated Detection of Alzheimer's Disease Using Brain MRI Images- A Study with Various Feature Extraction Techniques
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DOI：
10.1007/s10916-019-1428-9
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发表时间：
2019-08-09
影响因子：
5.3
通讯作者：
Yeong, Chai Hong
中科院分区：
文献类型：
--
作者：
Acharya, U. Rajendra;Fernandes, Steven Lawrence;Yeong, Chai Hong
这项工作的目的是开发一种计算机辅助脑诊断（CABD）系统，该系统可以确定脑部扫描是否显示阿尔茨海默病的迹象。该方法利用磁共振成像（MRI）和多种特征提取技术进行分类。核磁共振成像是一种非侵入性的程序，在医院被广泛用于检查认知异常。使用T2成像序列获取图像。该范式由一系列定量技术组成：过滤、特征提取、基于学生t检验的特征选择和基于k-最近邻（KNN）的分类。此外，通过实现文献中描述的其他特征提取程序进行比较分析。我们的研究结果表明，与其他方法相比，Shearlet变换（ST）特征提取技术为阿尔茨海默病的诊断提供了更好的结果。采用ST + KNN技术的CABD工具准确度为94.54%，精密度为88.33%，灵敏度为96.30%，特异性为93.64%。与基准MRI数据库相比，该工具的准确率、精密度、灵敏度和特异性分别为98.48%、100%、96.97%和100%。
The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer's disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student's t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer's diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database.