Breast cancer is considered the most serious cancer in women, among other prevalent cancer types. Chances of survival can be significantly increased through early detection, for which mammogram is considered to be the gold standard. This work addresses the development of a computer-aided diagnosis (CAD) system for analysis of mammographic masses. However, different mammographic tissue densities exhibit different characteristics and present abnormalities diversely. This renders a unified CAD framework for evaluation of masses, ineffective. To this end, we propose an ensemble framework for mass characterization, comprising different experts each specialized for a particular tissue category. Specifically, three segmentation-free feature descriptors including local binary pattern (LBP), scale-invariant feature transform (SIFT) and gray-level co-occurrence matrix (GLCM) are extracted from the regions of interest (ROIs), followed by individual classification with each feature descriptor using four different learning models, viz. support vector machine (SVM), artificial neural network (ANN), random forest (RF) and extreme grading boosting machine (XG Boost). All these 12 combinations are explored for each of the four breast density categories separately, to determine the best feature–classifier combination for a given category. The proposed ensemble scheme was validated on 1057 suspicious regions from digital database for screening mammograms (DDSM), demonstrating an improved performance when compared to state-of-the-art single learning framework modeled on all density categories collectively.

中文翻译：

---

用于乳房 X 光照片质量表征的密度特定专家集合

乳腺癌被认为是女性中最严重的癌症，以及其他常见的癌症类型。通过早期检测可以显着增加生存机会，乳房 X 光检查被认为是金标准。这项工作致力于开发用于分析乳腺肿块的计算机辅助诊断 (CAD) 系统。然而，不同的乳腺组织密度表现出不同的特征并呈现出不同的异常。这使得用于评估质量的统一 CAD 框架无效。为此，我们提出了一个用于质量表征的整体框架，由不同的专家组成，每个专家专门针对特定的组织类别。具体来说，包括局部二值模式（LBP）在内的三个无分割特征描述符，从感兴趣区域 (ROI) 中提取尺度不变特征变换 (SIFT) 和灰度共生矩阵 (GLCM)，然后使用四种不同的学习模型对每个特征描述符进行单独分类，即。支持向量机（SVM）、人工神经网络（ANN）、随机森林（RF）和极限分级提升机（XG Boost）。分别针对四个乳房密度类别中的每一个探索所有这 12 种组合，以确定给定类别的最佳特征分类器组合。所提出的集成方案在数字数据库中的 1057 个可疑区域上进行了验证，用于筛查乳房 X 光照片 (DDSM)，与基于所有密度类别共同建模的最先进的单一学习框架相比，表现出更高的性能。