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The usefulness of ultrasound in the classification of chronic liver disease

机译：超声在慢性肝病分类中的作用

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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%.
机译：慢性肝病是一种进行性疾病，在大多数情况下是无症状的，并且可能是致命的疾病。在本文中，基于超声肝图像，临床和实验室数据，提出了一种半自动程序对该疾病进行分期。在算法的核心中，使用了两个分类器：k个最近的邻居和支持向量机（具有不同的内核）。使用建议的多模式特征集对分类器进行训练，并将获得的结果与实验室和临床特征集进行比较。结果表明，使用基于超声的特征以及实验室和临床特征，可以提高分类的准确性。支持向量机（多项式内核）在所研究的每个类别中的表现均优于其他分类器。对于普通班，我们达到了100％的准确性，对于患有肝硬化的慢性肝炎，达到了73.08％，对于补偿性肝硬化，达到了59.26％，对于失代偿性肝硬化，达到了91.67％。

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