  This paper proposes a CNN classification network based on Bagging and
stacking ensemble learning methods for breast cancer classification. The model
was trained and tested on the public dataset of DDSM. The model is capable of
fast and accurate classification of input images. According to our research
results, for binary classification (presence or absence of breast cancer), the
accuracy reached 98.84%, and for five-class classification, the accuracy
reached 98.34%. The model also achieved a micro-average recall rate of 94.80%
and an F1 score of 94.19%. In comparative experiments, we compared the effects
of different values of bagging_ratio and n_models on the model, as well as
several methods for ensemble bagging models. Furthermore, under the same
parameter settings, our BSECNN outperformed VGG16 and ResNet-50 in terms of
accuracy by 8.22% and 6.33% respectively.
