  To improve the recognition ability of computer-aided breast mass
classification among mammographic images, in this work we explore the
state-of-the-art classification networks to develop an ensemble mechanism.
First, the regions of interest (ROIs) are obtained from the original dataset,
and then three models, i.e., XceptionNet, DenseNet, and EfficientNet, are
trained individually. After training, we ensemble the mechanism by summing the
probabilities outputted from each network which enhances the performance up to
5%. The scheme has been validated on a public dataset and we achieved accuracy,
precision, and recall 88%, 85%, and 76% respectively.
