文章基本信息

标题：Genetic Fuzzy System (GFS) based wavelet co-occurrence feature selection in mammogram classification for breast cancer diagnosis
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作者：Meenakshi M. Pawar ; Meenakshi M. Pawar ; Sanjay N. Talbar 等
期刊名称：Perspectives in Science
印刷版ISSN：2213-0209
电子版ISSN：2213-0209
出版年度：2016
卷号：8
页码：247-250
DOI：10.1016/j.pisc.2016.04.042
语种：English
出版社：Elsevier
摘要：Summary Breast cancer is significant health problem diagnosed mostly in women worldwide. Therefore, early detection of breast cancer is performed with the help of digital mammography, which can reduce mortality rate. This paper presents wrapper based feature selection approach for wavelet co-occurrence feature (WCF) using Genetic Fuzzy System (GFS) in mammogram classification problem. The performance of GFS algorithm is explained using mini-MIAS database. WCF features are obtained from detail wavelet coefficients at each level of decomposition of mammogram image. At first level of decomposition, 18 features are applied to GFS algorithm, which selects 5 features with an average classification success rate of 39.64%. Subsequently, at second level it selects 9 features from 36 features and the classification success rate is improved to 56.75%. For third level, 16 features are selected from 54 features and average success rate is improved to 64.98%. Lastly, at fourth level 72 features are applied to GFS, which selects 16 features and thereby increasing average success rate to 89.47%. Hence, GFS algorithm is the effective way of obtaining optimal set of feature in breast cancer diagnosis.
关键词：Digital mammogram; Genetic Fuzzy System algorithm; Feature selection;