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Lung Nodule Classification Using Supervised Manifold Learning Based on All-Class

机译:基于全分类的监督流形学习对肺结节的分类

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Dimensionality reduction plays an important role in lung nodule classification, but in most of the existing methods, dimensionality is reduced with all classes being considered jointly, difference between feature subsets of different classes is ignored. In this paper, a supervised manifold feature extraction method based on fusion of all-class and pair wise-class is proposed. Firstly, a manifold learning method will be improved with category information being used fully. Secondly, features will be extracted by the improved manifold learning method and the feature subsets is divided into several parts, one is based on all-class structure, others based on each pair of classes. Finally, All-class subset is used in K-nearest (KNN) classifiers, others used in Support Vector Machine (SVM) classifiers, and an supervised multi-classifiers system of lung nodule classification is constructed. Experiments show a significant improvement in recognition accuracy.
机译:降维在肺结节的分类中起着重要的作用,但是在大多数现有方法中,降维是在联合考虑所有类别的情况下降低的,而忽略了不同类别的特征子集之间的差异。提出了一种基于全类和逐对类融合的有监督流形特征提取方法。首先,将充分利用类别信息来改进流形学习方法。其次,通过改进的流形学习方法提取特征,并将特征子集划分为几个部分,一个基于全类结构,其他基于每对类。最后,将全分类子集用于K近邻(KNN)分类器中,将其他分类器用于支持向量机(SVM)分类器中,并构建有监督的肺结节分类多分类器系统。实验表明,识别精度有了显着提高。

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