  In this work, we present a deep learning framework for multi-class breast
cancer image classification as our submission to the International Conference
on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer
Histology images (BACH). As these histology images are too large to fit into
GPU memory, we first propose using Inception V3 to perform patch level
classification. The patch level predictions are then passed through an ensemble
fusion framework involving majority voting, gradient boosting machine (GBM),
and logistic regression to obtain the image level prediction. We improve the
sensitivity of the Normal and Benign predicted classes by designing a Dual Path
Network (DPN) to be used as a feature extractor where these extracted features
are further sent to a second layer of ensemble prediction fusion using GBM,
logistic regression, and support vector machine (SVM) to refine predictions.
Experimental results demonstrate our framework shows a 12.5$\%$ improvement
over the state-of-the-art model.
