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首页> 外文期刊>Physics in medicine and biology. >Decision optimization of case-based computer-aided decision systems using genetic algorithms with application to mammography.
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Decision optimization of case-based computer-aided decision systems using genetic algorithms with application to mammography.

机译：基于案例的计算机辅助决策系统的决策优化，使用遗传算法并将其应用于乳腺摄影。

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This paper presents an optimization framework for improving case-based computer-aided decision (CB-CAD) systems. The underlying hypothesis of the study is that each example in the knowledge database of a medical decision support system has different importance in the decision making process. A new decision algorithm incorporating an importance weight for each example is proposed to account for these differences. The search for the best set of importance weights is defined as an optimization problem and a genetic algorithm is employed to solve it. The optimization process is tailored to maximize the system's performance according to clinically relevant evaluation criteria. The study was performed using a CAD system developed for the classification of regions of interests (ROIs) in mammograms as depicting masses or normal tissue. The system was constructed and evaluated using a dataset of ROIs extracted from the Digital Database for Screening Mammography (DDSM). Experimental results show that, according to receiver operator characteristic (ROC) analysis, the proposed method significantly improves the overall performance of the CAD system as well as its average specificity for high breast mass detection rates.
机译：本文提出了一种用于改进基于案例的计算机辅助决策（CB-CAD）系统的优化框架。该研究的基本假设是，医疗决策支持系统的知识数据库中的每个示例在决策过程中具有不同的重要性。为解决这些差异，提出了一种新的决策算法，该算法结合了每个示例的重要性权重。对最佳重要权重集的搜索被定义为优化问题，并采用遗传算法对其进行求解。根据临床相关评估标准对优化过程进行了定制，以使系统性能最大化。这项研究是使用CAD系统进行的，该系统开发用于对乳房X线照片中的肿块或正常组织进行感兴趣区域（ROI）的分类。该系统是使用从乳腺X线筛查数字数据库（DDSM）中提取的ROI数据集构建和评估的。实验结果表明，根据接收者操作员特征（ROC）分析，该方法显着提高了CAD系统的整体性能以及其对高乳房质量检出率的平均特异性。

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