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Clustered micro calcifications (MCs) are one of the early signs of breast cancer. In this paper, we propose a new computer aided diagnosis (CAD) system for automatic detection of MCs in two steps. First, pixels corresponding to potential micro calcifications are found using a multilayer feed-forward neural network. The input of this network consists of 4 wavelet and 2 gray-level features. The output...
Clustered microcalcifications (MCs) in mammograms can be an important early sign of breast cancer in women. Their accurate detection is an important problem in computer aided detection. To improve the performance of detection, we propose a bagging-based twin support vector machine (B-TWSVM) to detect MCs. The ground truth of MCs in mammograms is assumed to be known as a priori. First each MCs is preprocessed...
Clusters of microcalcifications in mammograms are an important early sign of breast cancer in women. In this paper an approach is proposed to develop a computer-aided diagnosis (CAD) system that can be very helpful for radiologist in diagnosing microcalcifications' patterns in digitized mammograms earlier and faster than typical screening programs. The proposed method has been implemented in three...
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