  Traditional breast cancer image classification methods require manual
extraction of features from medical images, which not only require professional
medical knowledge, but also have problems such as time-consuming and
labor-intensive and difficulty in extracting high-quality features. Therefore,
the paper proposes a computer-based feature fusion Convolutional neural network
breast cancer image classification and detection method. The paper pre-trains
two convolutional neural networks with different structures, and then uses the
convolutional neural network to automatically extract the characteristics of
features, fuse the features extracted from the two structures, and finally use
the classifier classifies the fused features. The experimental results show
that the accuracy of this method in the classification of breast cancer image
data sets is 89%, and the classification accuracy of breast cancer images is
significantly improved compared with traditional methods.
