文章基本信息

标题：Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies
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作者：Łukasz Jeleń ; Thomas Fevens ; Adam Krzyżak 等
期刊名称：International Journal of Applied Mathematics and Computer Science
电子版ISSN：2083-8492
出版年度：2008
卷号：18
期号：1
DOI：10.2478/v10006-008-0007-x
出版社：De Gruyter Open
摘要：According to the World Health Organization (WHO), breast cancer (BC) is one of the most deadly cancers diagnosed among middle-aged women. Precise diagnosis and prognosis are crucial to reduce the high death rate. In this paper we present a framework for automatic malignancy grading of fine needle aspiration biopsy tissue. The malignancy grade is one of the most important factors taken into consideration during the prediction of cancer behavior after the treatment. Our framework is based on a classification using Support Vector Machines (SVM). The SVMs presented here are able to assign a malignancy grade based on preextracted features with the accuracy up to 94.24%. We also show that SVMs performed best out of four tested classifiers
关键词：automated malignancy grading; FNA grading; SVM; breast cancer grading; Bloom-Richardson