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To improve the recognition ability of computer-aided breast massclassification among mammographic images, in this work we explore thestate-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, aretrained individually. After training, we ensemble the mechanism by summing theprobabilities outputted from each network which enhances the performance up to5%. The scheme has been validated on a public dataset and we achieved accuracy,precision, and recall 88%, 85%, and 76% respectively.

Ensemble CNNs for Breast Tumor Classification