Breast cancer is the growth of a malignant tumor in the breast. The incidence of this disease in women has increased significantly in recent years. Currently, early detection is an important factor in cancer treatment. The most effective method for early detection is through mammography’s images. The computer-aided diagnosis systems are essential to help searching for suspicious signs, or classifying lesions in benign or malignant types. In this paper, a new method is designed for mass detection and classification based on weighted association rule mining (WARM). The main purpose of this study is to focus on the segmentation and classification and to provide a solution to optimize the accuracy of detection and classification of masses in mammography images to classify the masses in mammography images into two classes, benign and malignant. The results show proposed model in terms of accuracy, sensitivity and specificity achieved superior in comparison with several baselines.

乳腺癌是一种恶性肿瘤在乳房中生长。近年来，该病在女性中的发病率显着增加。目前，早期发现是癌症治疗的重要因素。早期检测的最有效方法是通过乳房 X 光检查的图像。计算机辅助诊断系统对于帮助搜索可疑迹象或将病变分类为良性或恶性类型至关重要。在本文中，设计了一种基于加权关联规则挖掘（WARM）的质量检测和分类新方法。本研究的主要目的是专注于分割和分类，并提供一种优化乳房X线图像中肿块检测和分类精度的解决方案，将乳房X线图像中的肿块分为良性和恶性两类。