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Performance evaluation of clustering algorithms on microcalcifications as mammography findings

机译:乳腺微钙化聚类算法的性能评估

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摘要

Breast cancer can be prevented with regular mammography screening. Yet, the incorporation of Computational Intelligence relies on training classifiers on a set of predefined Regions of Interest (ROIs). Data Clustering has been applied to address the problem of ROI detection, yet no extensive research has been carried out on which algorithm to utilize. This contribution focuses on microcalcification clustering as a Data Clustering application, giving insights concerning the performance of three main clustering algorithms.
机译:定期进行乳腺X线摄影检查可以预防乳腺癌。然而,计算智能的并入依赖于一组预定义的兴趣区域(ROI)上的训练分类器。数据聚类已被用于解决ROI检测的问题,但是尚未对使用哪种算法进行广泛的研究。此贡献着重于将微钙化聚类作为数据聚类应用程序,提供有关三种主要聚类算法性能的见解。

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