Ribeiro Ricardo, Marinho Rui Tato, Velosa José, Ramalho Fernando, Sanches J Miguel, Suri Jasjit S
Escola Superior de Tecnologia da Saúde de Lisboa and Institute for Systems and Robotics / Instituto Superior Tecnico.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:5132-5. doi: 10.1109/IEMBS.2011.6091271.
Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.
慢性肝病是一种渐进性疾病，多数情况下无症状，但可能致命。本文提出了一种基于肝脏超声图像、临床和实验室数据对该疾病进行分期的半自动程序。该算法的核心使用了两个分类器：一个k近邻分类器和一个支持向量机，支持向量机采用了不同的核函数。使用所提出的多模态特征集对分类器进行训练，并将所得结果与实验室和临床特征集进行比较。结果表明，结合实验室和临床特征使用基于超声的特征可提高分类准确率。在研究的每个类别中，支持向量机的多项式核函数表现优于其他分类器。对于正常类别，我们实现了100%的准确率，对于伴有肝硬化的慢性肝炎为73.08%，对于代偿性肝硬化为59.26%，对于失代偿性肝硬化为91.67%。
