基于纹理特征和支持向量机的宫颈癌前病变分类系统

Y. Jusman, Brilian Permata Sari, S. Riyadi
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引用次数: 2

摘要

宫颈癌是女性生殖健康疾病之一,是全球的一个重大问题,因为有大量新病例和死亡,特别是在发展中国家的妇女中。如果及早发现,子宫颈癌是可以避免的。在工业化国家,巴氏涂片检查被用于早期发现宫颈癌。然而,有限的人力资源、大量的时间投入、高昂的价格和不足的基础设施使其在发展中国家不太成功。本研究针对正常、低级别鳞状上皮内病变(LSIL)和高级别鳞状上皮内病变(HSIL)三种宫颈细胞图像,采用灰度共生矩阵(GLCM)图像处理技术和支持向量机(SVM)分类方法(HSIL)对宫颈细胞图像进行分类。分类系统以HSIL类为正数据,LSIL和Normal为负数据,使用三种SVM模型:Cubic, Quadratic, Fine Gaussian。使用GLCM特征提取方法,SVM分类准确率为97.5% (3.54s)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cervical Precancerous Classification System based on Texture Features and Support Vector Machine
Cervical cancer is one of the female reproductive health diseases being a significant issue globally because of the large number of new cases and deaths, particularly among women in developing countries. Cervical cancer can be avoided if detected early. The Pap smear screening procedure is used in industrialized nations to detect cervical cancer early. However, limited human resources, a significant time commitment, high prices, and insufficient infrastructure make it less successful in developing countries. With three types of cervical cell images: Normal, Low-grade Squamous Intraepithelial Lesion (LSIL), and High-grade Squamous Intraepithelial Lesion (HSIL), this study offers a classification system for cervical cell images using an image processing technique called Gray Level Co-occurrence Matrix (GLCM) and a Support Vector Machine (SVM) classification method (HSIL). With HSIL class as positive data and LSIL and Normal as negative data, the classification system used three SVM models: Cubic, Quadratic, and Fine Gaussian. SVM classification accuracy was 97.5 percent for 3.54s using the GLCM feature extraction approach.
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