Breast density may be used as a predictor of breast cancer risk and can measure the condition of tissues on mammograms. This research developed a computer-aided diagnosis (CAD) system to predict breast density on digital breast tomosynthesis (DBT) images. We used two-dimensional (2D) mammograms to train the linear discriminant analysis (LDA) classifier. Then load the DBT projection image to predict breast density. Experimental results show that LDA is better than other classification methods, such as one rule, naive Bayes, decision tree and support vector machine. The breast density prediction accuracy of DBT is 80%.

中文翻译：

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从 2D 乳房 X 光照片预测数字乳房断层合成的乳房密度

乳房密度可用作乳腺癌风险的预测指标，并可通过乳房 X 光检查测量组织状况。这项研究开发了一种计算机辅助诊断 (CAD) 系统，用于预测数字乳腺断层合成 (DBT) 图像上的乳腺密度。我们使用二维 (2D) 乳房 X 光检查来训练线性判别分析 (LDA) 分类器。然后加载 DBT 投影图像来预测乳腺密度。实验结果表明LDA优于其他分类方法，如单规则、朴素贝叶斯、决策树和支持向量机。DBT的乳腺密度预测准确率为80%。