Machine Learning
Output-Sensitive Algorithms for Computing Nearest-Neighbour Decision Boundaries
Discrete & Computational Geometry
Computers in Biology and Medicine
Mass Detection in Digital Mammograms Using Twin Support Vector Machine-Based CAD System
ICIE '09 Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 01
Computers in Biology and Medicine
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Mammography is currently the most effective imaging modality for early detection of breast cancer. In a CAD system for masses based on mammography, a mammogram is segmented to detect the masses. The segmentation gives rise to mass regions of interested ROIs, which are either benign or malignant. There is a need to classify the extracted mass ROIs into benign and malignant masses; it is a hard problem because the texture micro-structures of benign and malignant masses have close resemblance. In this paper, a method for classifying mass ROIs into benign and malignant masses is presented. The key idea of the proposal is to build an ensemble classifier that employs Gabor features, consults different experts classifiers and takes the final decision based on majority vote. The system is evaluated on 512 256 benign+256 malignant mass ROIs extracted from mammograms of DDSM database. The ensemble classifier improves the classification rate for the problem of the discrimination of benign and malignant masses to 90.64%. Comparison with state-of-the-art techniques suggests that the proposed system outperforms similar methods.