Comparison of Feature selection methods for diagnosis of cervical cancer using SVM classifier

B. Ashok; Dr. P. Aruna

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Comparison of Feature selection methods for diagnosis of cervical cancer using SVM classifier

机译：支持向量机分类器在宫颈癌诊断中的特征选择方法比较

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Even though a great attention has been given on the cervical cancer diagnosis, it is a tuff task to observe the pap smear slide through microscope. Image Processing and Machine learning techniques helps the pathologist to take proper decision. In this paper, we presented the diagnosis method using cervical cell image which is obtained by Pap smear test. Image segmentation performed by multi-thresholding method and texture and shape features are extracted related to cervical cancer. Feature selection is achieved using Mutual Information(MI), Sequential Forward Search (SFS), Sequential Floating Forward Search (SFFS) and Random Subset Feature Selection(RSFS) methods.

机译：即使已经对宫颈癌的诊断给予了极大的关注，但是通过显微镜观察宫颈涂片的滑动仍是一项艰巨的任务。图像处理和机器学习技术可帮助病理学家做出正确的决定。在本文中，我们提出了通过子宫颈抹片检查获得的宫颈细胞图像的诊断方法。通过多阈值方法进行图像分割，提取与宫颈癌有关的纹理和形状特征。使用互信息（MI），顺序向前搜索（SFS），顺序浮动向前搜索（SFFS）和随机子集特征选择（RSFS）方法可实现特征选择。

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《International Journal of Engineering Research and Applications》 |2016年第1期|共6页
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B. Ashok; Dr. P. Aruna;
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1. Comparison of Feature selection methods for diagnosis of cervical cancer using SVM classifier [J] . B. Ashok, Dr. P. Aruna International Journal of Engineering Research and Applications . 2016,第1期

机译：支持向量机分类器在宫颈癌诊断中的特征选择方法比较
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Comparison of Feature selection methods for diagnosis of cervical cancer using SVM classifier

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