This book aims to develop an intelligent breast cancer identification (ICBIS) system based on image processing techniques and neural network classifier. Recently, many researchers have developed image recognition systems for classifying breast cancer tumors using different image processing and classification techniques. The challenge is the extraction of the real features that distinguish the benign and malignant tumor. The classifications of breast cancer images have been performed using the shape and texture characteristics of the images. The asymmetry, roundness, intensity levels and more are the exact shape and texture features that distinguish the two types of breast tumors. Image processing techniques are used in order to detect tumor and extract the region of interest from the mammogram. The following data processing operations have been done for detection of images: thresholding, filtering and adjustments, canny edge detection, and some morphological operations. Shape and texture features are then extracted using GLCM (Gray-Level Co-Occurrence Matrix) algorithm in order to accurately classify the mammograms into normal, benign, and malignant tumors.
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