Wavelet energy entropy and linear regression classifier for detecting abnormal breasts

Yi Chen, Yin Zhang, Hui Min Lu, Xian Qing Chen, Jian Wu Li, Shui Hua Wang*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

30 引用 (Scopus)

摘要

Breast abnormalities are the early symptoms of breast cancers. They may also bring in psychoemotional stresses to women. In this study, we developed a new automatic program based on wavelet energy entropy (WEE) and linear regression classifier (LRC): First, we segment region of interest from mammogram images. Second, we calculate WEE from the segmented images. Third, LRC was used as the classifier. We named our method as “WEE + LRC”. The experiment used 10-fold stratified cross validation that was repeated 10 times. The statistical results showed the classification result was the best when the decomposition level was 4, with a sensitivity of 92.00 ± 3.20%, a specificity of 91.70 ± 3.27%, and an accuracy of 91.85 ± 2.21%. The proposed method was superior to other five state-of-the-art methods. In all, our method is effective in detecting abnormal breasts.

源语言英语
页(从-至)3813-3832
页数20
期刊Multimedia Tools and Applications
77
3
DOI
出版状态已出版 - 1 2月 2018

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