  Background: Breast cancer has the highest prevalence in women globally. The
classification and diagnosis of breast cancer and its histopathological images
have always been a hot spot of clinical concern. In Computer-Aided Diagnosis
(CAD), traditional classification models mostly use a single network to extract
features, which has significant limitations. On the other hand, many networks
are trained and optimized on patient-level datasets, ignoring the application
of lower-level data labels.
  Method: This paper proposes a deep ensemble model based on image-level labels
for the binary classification of benign and malignant lesions of breast
histopathological images. First, the BreaKHis dataset is randomly divided into
a training, validation and test set. Then, data augmentation techniques are
used to balance the number of benign and malignant samples. Thirdly,
considering the performance of transfer learning and the complementarity
between each network, VGG16, Xception, ResNet50, DenseNet201 are selected as
the base classifiers.
  Result: In the ensemble network model with accuracy as the weight, the
image-level binary classification achieves an accuracy of $98.90\%$. In order
to verify the capabilities of our method, the latest Transformer and Multilayer
Perception (MLP) models have been experimentally compared on the same dataset.
Our model wins with a $5\%-20\%$ advantage, emphasizing the ensemble model's
far-reaching significance in classification tasks.
  Conclusion: This research focuses on improving the model's classification
performance with an ensemble algorithm. Transfer learning plays an essential
role in small datasets, improving training speed and accuracy. Our model has
outperformed many existing approaches in accuracy, providing a method for the
field of auxiliary medical diagnosis.
