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首页> 外文期刊>Physics in medicine and biology. >An SVM classifier to separate false signals from microcalcifications in digital mammograms.
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An SVM classifier to separate false signals from microcalcifications in digital mammograms.

机译：SVM分类器可将错误的信号与数字乳房X线照片中的微钙化相分离。

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摘要

In this paper we investigate the feasibility of using an SVM (support vector machine) classifier in our automatic system for the detection of clustered microcalcifications in digital mammograms. SVM is a technique for pattern recognition which relies on the statistical learning theory. It minimizes a function of two terms: the number of misclassified vectors of the training set and a term regarding the generalization classifier capability. We compare the SVM classifier with an MLP (multi-layer perceptron) in the false-positive reduction phase of our detection scheme: a detected signal is considered either microcalcification or false signal, according to the value of a set of its features. The SVM classifier gets slightly better results than the MLP one (Az value of 0.963 against 0.958) in the presence of a high number of training data; the improvement becomes much more evident (Az value of 0.952 against 0.918) in training sets of reduced size. Finally, the setting of the SVM classifier is much easier than the MLP one.
机译：在本文中，我们研究了在我们的自动系统中使用SVM（支持向量机）分类器检测数字乳房X线照片中聚集的微钙化的可行性。 SVM是一种依赖于统计学习理论的模式识别技术。它最小化了两个术语的功能：训练集的错误分类向量的数量和一个关于泛化分类器能力的术语。我们在检测方案的假阳性还原阶段将SVM分类器与MLP（多层感知器）进行比较：根据一组特征的值，检测到的信号被视为微钙化或假信号。在有大量训练数据的情况下，SVM分类器比MLP分类器（Az值为0.963对0.958）获得更好的结果;在尺寸减小的训练集中，这种改进变得更加明显（Az值从0.952对0.918）。最后，SVM分类器的设置比MLP分类器容易得多。

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