Krawczyk Bartosz, Schaefer Gerald, Woźniak Michał
Department of Systems and Computer Networks, Wrocław University of Technology, Wyb. Wyspianskiego 27, 50-370 Wrocław, Poland.
Department of Computer Science, Loughborough University, Loughborough LE11 3TU, UK.
Artif Intell Med. 2015 Nov;65(3):219-27. doi: 10.1016/j.artmed.2015.07.005. Epub 2015 Jul 31.
Early recognition of breast cancer, the most commonly diagnosed form of cancer in women, is of crucial importance, given that it leads to significantly improved chances of survival. Medical thermography, which uses an infrared camera for thermal imaging, has been demonstrated as a particularly useful technique for early diagnosis, because it detects smaller tumors than the standard modality of mammography.
In this paper, we analyse breast thermograms by extracting features describing bilateral symmetries between the two breast areas, and present a classification system for decision making. Clearly, the costs associated with missing a cancer case are much higher than those for mislabelling a benign case. At the same time, datasets contain significantly fewer malignant cases than benign ones. Standard classification approaches fail to consider either of these aspects. In this paper, we introduce a hybrid cost-sensitive classifier ensemble to address this challenging problem. Our approach entails a pool of cost-sensitive decision trees which assign a higher misclassification cost to the malignant class, thereby boosting its recognition rate. A genetic algorithm is employed for simultaneous feature selection and classifier fusion. As an optimisation criterion, we use a combination of misclassification cost and diversity to achieve both a high sensitivity and a heterogeneous ensemble. Furthermore, we prune our ensemble by discarding classifiers that contribute minimally to the decision making.
For a challenging dataset of about 150 thermograms, our approach achieves an excellent sensitivity of 83.10%, while maintaining a high specificity of 89.44%. This not only signifies improved recognition of malignant cases, it also statistically outperforms other state-of-the-art algorithms designed for imbalanced classification, and hence provides an effective approach for analysing breast thermograms.
Our proposed hybrid cost-sensitive ensemble can facilitate a highly accurate early diagnostic of breast cancer based on thermogram features. It overcomes the difficulties posed by the imbalanced distribution of patients in the two analysed groups.
鉴于早期发现乳腺癌可显著提高生存率，而乳腺癌是女性中最常被诊断出的癌症形式，所以早期识别至关重要。医学热成像技术利用红外摄像机进行热成像，已被证明是一种特别有用的早期诊断技术，因为它能检测到比标准乳房X光检查更小的肿瘤。
在本文中，我们通过提取描述两个乳房区域之间双侧对称性的特征来分析乳房热成像图，并提出一个用于决策的分类系统。显然，漏诊癌症病例的代价远高于误诊良性病例的代价。同时，数据集中恶性病例比良性病例少得多。标准分类方法未能考虑到这两个方面。在本文中，我们引入一种混合成本敏感分类器集成方法来解决这个具有挑战性的问题。我们的方法需要一组成本敏感决策树，这些决策树为恶性类别分配更高的误分类成本，从而提高其识别率。采用遗传算法进行同步特征选择和分类器融合。作为优化标准，我们使用误分类成本和多样性的组合来实现高灵敏度和异构集成。此外，我们通过丢弃对决策贡献最小的分类器来修剪我们的集成。
对于一个包含约150张热成像图的具有挑战性的数据集，我们的方法实现了83.10%的出色灵敏度，同时保持了89.44%的高特异性。这不仅意味着对恶性病例的识别得到了改善，在统计上也优于其他为不平衡分类设计的先进算法，因此为分析乳房热成像图提供了一种有效方法。
我们提出的混合成本敏感集成方法能够基于热成像图特征实现对乳腺癌的高精度早期诊断。它克服了两个分析组中患者分布不平衡所带来的困难。
