Approximately 15% of all cancer deaths among women worldwide is due to breast cancer. Mammography is one of the most useful methods for the early detection of this disease. Over the last decade, several papers were published reporting the usage of different computer-aided diagnosis systems using pattern recognition techniques as a second opinion to obtain a more accurate diagnosis. However, the theory of formal languages has not been explored in this field. In this context, the main contribution of this study is to present the usage of a new syntactic approach that is able to classify breast masses found in mammograms as benign or malignant. The experimental tests were performed using a dataset that contains 111 images from different sources. The grammar-based classifiers achieved accuracy values ranging from 89% to 100% depending on the features and the model employed. Furthermore, to achieve a feature dimension reduction, a feature selection technique based on the Gini importance of each feature was employed. Additionally, we compared the obtained results with the grammar-based classifiers to the more traditional classifiers used in this research area, such as artificial neural networks, support vector machines, k-nearest neighbors, and random forest. The best result achieved by the grammar-based classifiers was approximately 10% higher, in terms of accuracy, than the best results produced by the traditional classifiers, showing the strength of this grammatical approach.

中文翻译：

---

使用语法对乳房 X 线照片中的质量进行自动分类的方法

全世界女性中大约 15% 的癌症死亡是由乳腺癌引起的。乳房X线照相术是早期发现这种疾病的最有用的方法之一。在过去的十年中，发表了几篇论文，报道了使用模式识别技术作为第二意见使用不同的计算机辅助诊断系统以获得更准确的诊断。然而，形式语言的理论尚未在该领域进行探索。在这种情况下，本研究的主要贡献是展示了一种新的句法方法的使用，该方法能够将乳房 X 线照片中发现的乳房肿块分类为良性或恶性。实验测试是使用包含来自不同来源的 111 幅图像的数据集进行的。根据所使用的特征和模型，基于语法的分类器实现了 89% 到 100% 的准确度值。此外，为了实现特征降维，采用了基于每个特征的基尼重要性的特征选择技术。此外，我们将获得的结果与基于语法的分类器与该研究领域中使用的更传统的分类器进行了比较，例如人工神经网络、支持向量机、k-最近邻和随机森林。就准确性而言，基于语法的分类器取得的最佳结果比传统分类器产生的最佳结果高约 10%，显示了这种语法方法的优势。采用了基于每个特征的基尼重要性的特征选择技术。此外，我们将获得的结果与基于语法的分类器与该研究领域中使用的更传统的分类器进行了比较，例如人工神经网络、支持向量机、k-最近邻和随机森林。就准确性而言，基于语法的分类器取得的最佳结果比传统分类器产生的最佳结果高约 10%，显示了这种语法方法的优势。采用了基于每个特征的基尼重要性的特征选择技术。此外，我们将获得的结果与基于语法的分类器与该研究领域中使用的更传统的分类器进行了比较，例如人工神经网络、支持向量机、k-最近邻和随机森林。就准确性而言，基于语法的分类器取得的最佳结果比传统分类器产生的最佳结果高约 10%，显示了这种语法方法的优势。