A hybrid classifier committee for analysing asymmetry features in breast thermograms
An effective approach to analysing breast thermograms for breast cancer diagnosis is presented. Image features describing bilateral asymmetry are used in a classification stage. A multiple classifier system based on neural networks/support vector machines as base classifiers, balanced subspaces to address class imbalance, a neural network fuser and a fuzzy diversity measure is presented. Experimental results show excellent classification performance on a challenging dataset and statistical superiority compared to other ensemble classifiers.
breast cancer image features multiple classifier system thermography pattern classification imbalanced classification