An Ensemble Approach for Classification of Breast Histopathology Images

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This paper presents an approach to classify breast histopathology images based on the mitotic count. An ensemble of handcrafted features, a bag of features (BoF), and convolutional neural network (CNN) are done for mitosis detection. Initially, mitotic regions are segmented followed by extraction of handcrafted features, BoF, and CNN-learned features from the candidate region. A non-linear support vector machine with Gaussian radial basis function kernel is used for the classification of each feature set independently. The proposed work utilizes the main tissue components of the images in an ensemble style to correctly classify the input histopathology image into benign or malignant. It also enables to maximize the performance by leveraging the disconnected feature set. Publicly available ICPR 2012 and 2014 mitosis dataset of is used for evaluation. The proposed ensemble method yielded a much improved F1 score. Better accuracy and fewer computing resources of our approach make it feasible for clinical use.