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Ontology Based Decision System for Breast Cancer Diagnosis

机译：基于本体的乳腺癌诊断决策系统

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In this paper, we focus on analysis and diagnosis of breast masses inspired by expert concepts and rules. Accordingly, a Bag of Words is built based on the ontology of breast cancer diagnosis, accurately described in the Breast Imaging Reporting and Data System. To fill the gap between low level knowledge and expert concepts, a semantic annotation is developed using a machine learning tool. Then, breast masses are classified into benign or malignant according to expert rules implicitly modeled with a set of classifiers (KNN, ANN, SVM and Decision Tree). This semantic context of analysis offers a frame where we can include external factors and other meta-knowledge such as patient risk factors as well as exploiting more than one modality. Based on MRI and DECEDM modalities, our developed system leads a recognition rate of 99.7% with Decision Tree where an improvement of 24.7 % is obtained owing to semantic analysis.
机译：在本文中，我们专注于受专家概念和规则启发的乳腺肿块的分析和诊断。因此，根据乳腺癌诊断的本体论构建了“一句话袋”，这在“乳房成像报告和数据系统”中得到了准确描述。为了填补基础知识与专家概念之间的空白，使用机器学习工具开发了语义注释。然后，根据使用一组分类器（KNN，ANN，SVM和决策树）隐式建模的专家规则，将乳房肿块分为良性或恶性。这种语义分析环境提供了一个框架，我们可以在其中包括外部因素和其他元知识，例如患者风险因素，以及利用多种方式。基于MRI和DECEDM模式，我们开发的系统通过决策树的识别率达到了99.7％，由于语义分析，识别率提高了24.7％。

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