Effective multiple classifier systems for breast thermogram analysis

Breast cancer is the most commonly diagnosed form of cancer in women. Thermography, which uses cameras with sensitivities in the thermal infrared, has been shown to provide an interesting modality for detecting breast cancer as it is able to detect small tumors and hence can lead to earlier diagnosis. In this paper, we present an effective approach to breast thermogram analysis that utilises features describing bilateral symmetries from an image, and utilises a classifier ensemble for decision making. Importantly, our classification approach addresses the problem of imbalanced class distribution that is common in medical decision making. We do this by constructing feature subspaces from balanced data subsets and train different classifiers on different subspaces. To combine the individual classifiers, we use a neural network as classifier fuser. We show our approach to work well and to lead to significantly improved performance compared to canonical classifiers and classifier ensembles.