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首页> 外文期刊>Advances in Science, Technology and Engineering Systems >Application of Feature Extraction for Breast Cancer using One Order Statistic, GLCM, GLRLM, and GLDM
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Application of Feature Extraction for Breast Cancer using One Order Statistic, GLCM, GLRLM, and GLDM

机译：一阶统计量，GLCM，GLRLM和GLDM在乳腺癌特征提取中的应用

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The increasing number of breast cancer in recent years has attracted numerous researchers’ attention. Several techniques of Computer Aided Diagnosis System have been proposed as alternative solutions to diagnose breast cancer. The flaw of simply using the naked eye to see the differences between normal and with cancer mammogram images makes the texture analysis play an important role in classifying breast cancer. In this study, the results of the classification were compared using various methods of texture analysis in extracting a feature of the mammogram image. Some texture analysis methods, including first order, which consist of GLCM, GLRLM, and GLDM, have successfully extracted features based on their characteristics. The statistical features of these methods are used as input for the ECOC SVM classification, which three kernel comparisons; linear, RBF, and polynomial, build the classification. The results show that the best kernel is polynomial kernels with statistical features built by GLRLM with 93.9757% accuracy value.
机译：近年来，越来越多的乳腺癌引起了众多研究人员的关注。已经提出了几种计算机辅助诊断系统的技术作为诊断乳腺癌的替代解决方案。简单地用肉眼看到正常乳房X线照片与癌症乳房X线照片之间的差异的缺陷使得纹理分析在乳腺癌的分类中起着重要的作用。在这项研究中，使用各种纹理分析方法比较了分类结果，以提取乳房X线照片的特征。由GLCM，GLLRM和GLDM组成的一些纹理分析方法（包括一阶）已成功地根据特征提取了特征。这些方法的统计特征被用作ECOC SVM分类的输入，这是三个内核比较。线性，RBF和多项式可建立分类。结果表明，最佳核是由GLRLM建立的具有统计特征的多项式核，其准确度值为93.9757％。

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