A Novel Breast Cancer Detection Technology Using an Advanced Transfer Maximal Entropy Clustering Algorithm
A Novel Breast Cancer Detection Technology Using an Advanced Transfer Maximal Entropy Clustering Algorithm
复制标题
使用先进的转移最大熵聚类算法的新型乳腺癌检测技术
DOI：
10.1166/jmihi.2019.2775
复制
发表时间：
2019-10
影响因子：
--
通讯作者：
Zhou Leyuan
中科院分区：
文献类型：
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作者：
Peng Lifang;Huang Bin;Chen Kefu;Zhou Leyuan
乳腺癌的初步诊断包括分析患者的相关检查报告，以确定肿瘤是良性还是恶性。无监督聚类算法可以用于这类问题。在对病人检查结果的聚类分析中
数据，得到聚类结果和初步诊断结果。然而，由于检测成本高，医疗数据集通常具有较小的样本量或缺乏信息。传统的聚类技术在这类场景下聚类效果往往很差。解决
针对这一问题，在经典最大熵聚类算法的基础上，提出了一种改进的迁移学习机制，并提出了一种改进的迁移最大熵聚类算法（AT-MEC）。使用威斯康星州乳腺癌数据集进行模拟实验。
在威斯康星州乳腺癌数据集上验证了该算法的聚类效果优于其他聚类算法。
The initial diagnosis of breast cancer involves analyzing the relevant examination report of the patient to determine whether the tumor is benign or malignant. Unsupervised clustering algorithms can be used with this type of problem. In a cluster analysis of a patient's examination
data, the clustering results and the preliminary diagnosis results are obtained. However, due to the high cost of detection, medical datasets often have a small sample size or lack information. The traditional clustering technique usually has poor clustering effects in such scenarios. To solve
this problem, this paper proposes an advanced transfer learning mechanism based on the classic maximum entropy clustering algorithm and proposes an advanced transfer maximal entropy clustering (AT-MEC) algorithm. A simulation experiment using the Wisconsin Breast Cancer Dataset is performed.
This paper verifies that the proposed AT-MEC algorithm has a better clustering effect than other clustering algorithms in the Wisconsin Breast Cancer Dataset.