MCs Detection Approach Using Bagging and Boosting Based Twin Support Vector Machine
Affiliation of Author(s):
管理学院
Key Words:
中文关键字：—enseble learning,Boosting,feature extraction，英文关键字：—enseble learning,Boosting,feature extraction
Abstract:
In this paper we discuss a new approach for the
detection of clustered microcalcifications (MCs) in
mammograms. MCs are an important early sign of breast
cancer in women. Their accurate detection is a key issue in
computer aided detection scheme. To improve the performance
of detection, we propose a Bagging and Boosting based twin
support vector machine (BB-TWSVM) to detect MCs. The
algorithm is composed of three modules: the image pro-
processing, the feature extraction component and the BB-
TWSVM module. The ground truth of MCs in mammograms is
assumed to be known as a priori. First each MCs is preprocessed
by using a simple artifact removal filter and a well designed
high-pass filter. Then the combined image feature extractors are
employed to extract 164 image features. In the combined image
features space, the MCs detection procedure is formulated as a
supervised learning and classification problem, and the trained
BB-TWSVM is used as a classifier to make decision for the
presence of MCs or not. The experimental results of this study
indicate the potential of the approach for computer-aided
detection of breast cancer.
Paper Publications
