DBLCNN: Dependency-based lightweight convolutional neural network for multi-classification of breast histopathology images

Breast histopathology analysis is the gold standard for diagnosing breast cancer. Convolutional neural network based methods for breast histology image classification have emerged in recent years to make the analysis process simple and fast. Due to the limitation of hardware devices, these classification methods still face the problem of difficult balance recognition performance and computational efficiency. In this paper, we propose the Dependency-based lightweight convolutional neural network (DBLCNN) for the multi-classification task of breast histopathology images. Firstly, we design a new network in which dependencies (magnification and binary classification probability) were used to guide subspecies features for better recognition. Secondly, we redesign the backbone MobileNet to greatly reduce the model parameters and computation while ensuring excellent recognition performance. At the same time, transfer learning based on ImageNet is applied to the DBLCNN network. Extensive experiments on the BreakHis dataset have shown that the DBLCNN network has state-of-theart effects in terms of recognition performance and computational utilization.

Automated summary

This paper proposes a Dependency-based lightweight convolutional neural network (DBLCNN) for the multi-classification task of breast histopathology images. The network utilizes dependencies to guide features, redesigns the backbone network for improved recognition performance, and applies transfer learning. Extensive experiments show that the DBLCNN network achieves excellent recognition performance and computational utilization.