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To improve the performance of the computer-aided systems for breast cancer diagnosis, the ensemble classifier is proposed for classifying the histological structures in the breast cancer microscopic images into three region types: positive cancer cells, negative cancer cells and non-cancer cell (stromal cells and lymphocyte cells) image. The bagging and boosting ensemble techniques are used with the...
This study proposes and evaluates the application of two classifiers: decision tree (DT) and neural network (NN) to discriminate three region types: cancer (CC), lymphocyte (LC), and stromal (SC) in the breast cancer cell images. The feature extraction from area based texture information of BCCI is studied to compare results from the segmented cells. A combination between texture features based on...
This paper describes a computer-aided system for analyzing stained breast cancer cell images. The procedure for the analysis approach is composed of three steps. First, the cancer cells in the microscopic image are segmented based on neural network and mathematical morphology. Next, the features consisting of average values of L*; a*, b*, area and circularity ratio of each cell are extracted. Finally,...
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