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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 study proposes and appraise a gray level co-occurrence matrix (GLCM) for extracting the feature of cell regions in microscopic image into four region types: positive cancer cell, negative cancer cell, lymphocyte and stromal cell. The classification task uses decision tree with cross validation. To give a high classification performance, the main focus of interest is feature extraction task. Twenty-two...
This study proposes and evaluates a neural network (NN) classifier for dividing the histological structures (HS) in breast cancer (BC) microscopic image into two region types: cancer or normal. Cancer region included positive cells and negative cells while normal region included stromal cells and lymphocyte. The classification task using a back propagation learning algorithm is applied to the multilayer...
To explore application of fractal analysis to study texture features of microscopic images, a critical exponent analysis (CEA) method is proposed to improve classification ability of histological structures in microscopic breast cancer images based on one-dimensional (1D) sequences. Fractal analysis is commonly a mathematical tool for handling with a complex system. A method of estimating fractal...
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