  Breast cancer is one of the leading causes of death across the world in
women. Early diagnosis of this type of cancer is critical for treatment and
patient care. Computer-aided detection (CAD) systems using convolutional neural
networks (CNN) could assist in the classification of abnormalities. In this
study, we proposed an ensemble deep learning-based approach for automatic
binary classification of breast histology images. The proposed ensemble model
adapts three pre-trained CNNs, namely VGG19, MobileNet, and DenseNet. The
ensemble model is used for the feature representation and extraction steps. The
extracted features are then fed into a multi-layer perceptron classifier to
carry out the classification task. Various pre-processing and CNN tuning
techniques such as stain-normalization, data augmentation, hyperparameter
tuning, and fine-tuning are used to train the model. The proposed method is
validated on four publicly available benchmark datasets, i.e., ICIAR, BreakHis,
PatchCamelyon, and Bioimaging. The proposed multi-model ensemble method obtains
better predictions than single classifiers and machine learning algorithms with
accuracies of 98.13%, 95.00%, 94.64% and 83.10% for BreakHis, ICIAR,
PatchCamelyon and Bioimaging datasets, respectively.
