Highly reliable breast cancer diagnosis with cascaded ensemble classifiers

Accuracy and reliability are two important issues in computer assisted breast cancer diagnosis. In this paper, a new cascade Random Subspace ensembles scheme with reject options is proposed for automatic breast cancer diagnosis. The diagnosis system is built as a serial fusion of two different Random Subspace classifier ensembles with rejection options to enhance the classification reliability. The first ensemble consists of a set of Support Vector Machine (SVM) classifiers that converts the original K-class classification problem into a number of K 2-class problems. The second ensemble consists of a Multi-Layer Perceptron (MLP) ensemble, that focuses on the rejected samples from the first ensemble. For both of the ensembles, the reject option is implemented by relating the consensus degree from majority voting to a confidence measure, and abstaining to classify ambiguous samples if the consensus degree is lower than some threshold. Using a microscopic breast biopsy image dataset from Israel Institute of Technology and benchmark datasets from UCI, promising results are obtained using the proposed system.