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首页> 外文期刊>Computers in Biology and Medicine >A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation
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A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation

机译：基于统计的基于特征的数字乳腺X线摄影特征提取方法

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This paper presents a method for breast cancer diagnosis in digital mammogram images. Multiresolution representations, wavelet or curvelet, are used to transform the mammogram images into a long vector of coefficients. A matrix is constructed by putting wavelet or curvelet coefficients of each image in row vector, where the number of rows is the number of images, and the number of columns is the number of coefficients. A feature extraction method is developed based on the statistical t-test method. The method is ranking the features (columns) according to its capability to differentiate the classes. Then, a dynamic threshold is applied to optimize the number of features, which can achieve the maximum classification accuracy rate. The method depends on extracting the features that can maximize the ability to discriminate between different classes. Thus, the dimensionality of data features is reduced and the classification accuracy rate is improved. Support vector machine (SVM) is used to classify between the normal and abnormal tissues and to distinguish between benign and malignant tumors. The proposed method is validated using 5-fold cross validation. The obtained classification accuracy rates demonstrate that the proposed method could contribute to the successful detection of breast cancer.
机译：本文提出了一种在乳腺X线照片中诊断乳腺癌的方法。多分辨率表示（小波或曲波）用于将乳房X线照片转换为系数的长向量。通过将每个图像的小波或曲波系数放在行向量中来构造矩阵，其中行数是图像数，列数是系数数。基于统计t检验方法开发了特征提取方法。该方法根据其区分类的功能对要素（列）进行排名。然后，应用动态阈值来优化特征数量，从而可以实现最大的分类准确率。该方法取决于提取可以最大程度区分不同类别的功能的特征。因此，减少了数据特征的维数并提高了分类准确率。支持向量机（SVM）用于对正常组织和异常组织进行分类，并区分良性和恶性肿瘤。使用5倍交叉验证对提出的方法进行验证。获得的分类准确率表明，所提出的方法可以有助于成功检测乳腺癌。

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