Automatic classification of breast cancer histopathological images based on deep feature fusion and enhanced routing

Automatic classification of breast cancer histopathological images is of great application value in breast cancer diagnosis. Convolutional neural network (CNN) usually highlights semantics, while capsule network (CapsNet) focuses on detailed information about the position and posture. Combining these information can obtain more discriminative features which is useful to improve the classification performance. In the paper, breast cancer histopathological image classification based on deep feature fusion and enhanced routing (FE-BkCapsNet) is proposed to take advantages of CNN and CapsNet. First, a novel structure with dual channels which can extract convolution features and capsule features simultaneously, integrate sematic features and spatial features into new capsules to obtain more discriminative information is designed. Then, routing coefficients are optimized indirectly and adaptively by modifying the loss function and embedding the routing process into entire optimization process. The proposed method FE-BkCapsNet was tested on a public dataset BreaKHis. Experimental results (40x: 92.71%, 100x: 94.52%, 200x: 94.03%, 400x: 93.54) demonstrate that the proposed method is efficient for breast cancer classification in clinical settings.

Automated summary

The paper proposes a method for breast cancer histopathological image classification based on deep feature fusion and enhanced routing. The method was tested on the BreaKHis dataset, showing efficient performance for breast cancer classification in clinical settings.