Shu Ting, Zhang Bob, Tang Yuan Yan
Department of Computer and Information Science, University of Macau, Taipa, Macau.
Evid Based Complement Alternat Med. 2017;2017:7483639. doi: 10.1155/2017/7483639. Epub 2017 Aug 13.
At present, heart disease is the number one cause of death worldwide. Traditionally, heart disease is commonly detected using blood tests, electrocardiogram, cardiac computerized tomography scan, cardiac magnetic resonance imaging, and so on. However, these traditional diagnostic methods are time consuming and/or invasive. In this paper, we propose an effective noninvasive computerized method based on facial images to quantitatively detect heart disease. Specifically, facial key block color features are extracted from facial images and analyzed using the Probabilistic Collaborative Representation Based Classifier. The idea of facial key block color analysis is founded in Traditional Chinese Medicine. A new dataset consisting of 581 heart disease and 581 healthy samples was experimented by the proposed method. In order to optimize the Probabilistic Collaborative Representation Based Classifier, an analysis of its parameters was performed. According to the experimental results, the proposed method obtains the highest accuracy compared with other classifiers and is proven to be effective at heart disease detection.
目前，心脏病是全球头号死因。传统上，心脏病通常通过血液检测、心电图、心脏计算机断层扫描、心脏磁共振成像等方法来检测。然而，这些传统诊断方法既耗时又具有侵入性。在本文中，我们提出了一种基于面部图像的有效的非侵入性计算机化方法来定量检测心脏病。具体而言，从面部图像中提取面部关键块颜色特征，并使用基于概率协作表示的分类器进行分析。面部关键块颜色分析的理念源于中医。所提出的方法对一个由581个心脏病样本和581个健康样本组成的新数据集进行了实验。为了优化基于概率协作表示的分类器，对其参数进行了分析。根据实验结果，所提出的方法与其他分类器相比获得了最高的准确率，并被证明在心脏病检测方面是有效的。
