Early detection of breast pathologies and proper treatment increase the likelihood of a cure, and, as a result, life expectancy. Currently, methods and algorithms for computer aided detection (CAD) systems are being actively developed. The traditional approach to designing such systems consists in selecting and calculating the features of the region of interest from the source data, followed by the selection of a model for their classification using machine learning methods. This paper proposes a method for detecting and classifying breast anomalies based on local energy and phase congruency and a controlled machine learning classifier. Experimental results are presented using a digital mammography dataset and evaluated using various performance criteria.

早期发现乳房病变并进行适当治疗会增加治愈的可能性，并因此增加预期寿命。当前，正在积极地开发用于计算机辅助检测（CAD）系统的方法和算法。设计此类系统的传统方法包括从源数据中选择并计算感兴趣区域的特征，然后使用机器学习方法选择模型进行分类。本文提出了一种基于局部能量和相位一致性的检测和分类乳房异常的方法以及一种受控的机器学习分类器。使用数字乳腺X射线摄影数据集展示实验结果，并使用各种性能标准对其进行评估。