Strategies for addressing class imbalance in ensemble classification of thermography breast cancer features

Thermography provides an interesting alternative to mammography for diagnosing breast cancer as it is a noncontact, non-invasive and passive technique that is able to detect small tumors and thus can lead to earlier diagnosis. Computer-aided diagnostic approaches based on thermography are typically split into a feature extraction stage that derives useful information from the thermogram images, and a classification stage that distinguishes between malignant and benign cases. The latter is challenging since, as is the case for many medical decision making problems, there are (many) more benign cases available for classifier training compared to malignant cases, leading to an imbalanced classification problem. In this paper, we first perform image analysis to identify features describing bilateral differences in regions of interest in the thermogram. These features then form the input for a pattern classification stage for which we present several strategies to address the existing class imbalance in the context of ensemble classifiers. In particular, we discuss an ensemble constructed of cost-sensitive decision tree classifiers, an ensemble whose base classifiers are trained on balanced subspaces, and an ensemble that is based on the combination of one-class classifiers. All three strategies are evaluated on a challenging dataset of about 150 thermograms and it is shown that they provide very good classification performance and furthermore perform favourably compared to other state-of-the-art classifier ensembles for imbalanced data.