首页> 外文会议>International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation >Automatic Detection of Breast Cancer in Mammographic Image Using the Histogram Oriented Gradient (HOG) Descriptor and Deep Rule Based (DRB) Classifier Method
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Automatic Detection of Breast Cancer in Mammographic Image Using the Histogram Oriented Gradient (HOG) Descriptor and Deep Rule Based (DRB) Classifier Method

机译:直方图梯度(HOG)描述符和基于深度规则(DRB)的分类器方法在乳腺X线图像中自动检测乳腺癌

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Breast cancer is a cell disease in the breast glands that evolve abnormally and become benign or malignant tumors. Breast cancer is a common disease in developing countries and refers to malignancies that can be life-threatening. Mammographic imaging examination is performed to determine the mass of the breast lump to be analyzed for its malignancy. In this research, digital image processing is performed to diagnose breast cancer using mammographic images and feature extraction data with Histogram of Oriented Gradient (HOG), which is further classified using Deep Rule Based (DRB) Classifier. This research was conducted by dividing the data 90% for training data and 10% for testing data. Data are classified into three classes, consisting of normal, benignant, and malignant. The best accuracy obtained is 92.00%.
机译:乳腺癌是乳腺中的一种细胞疾病,会异常发展并变成良性或恶性肿瘤。乳腺癌是发展中国家的常见疾病,是指可能威胁生命的恶性肿瘤。进行乳房X光检查以确定要分析其恶性的乳房肿块的质量。在这项研究中,使用乳腺X射线照片和定向梯度直方图(HOG)对特征提取数据进行数字图像处理,以诊断乳腺癌,然后使用基于深度规则的分类器(DRB)对其进行分类。这项研究是通过将训练数据的90%和测试数据的10%相除而进行的。数据分为正常,良性和恶性三类。获得的最佳精度为92.00%。

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