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

Chaoqing Wang, Weijun Gong, Junlong Cheng, Yurong Qian

Abstract: Highlights•We implement magnification-independent multi-classification methods on the BreakHis dataset. We investigate the relationship between model computational efficiency and recognition performance.•We design the DBLCNN network, which exploits the dependencies to efficiently guide the convolutional features to achieve better feature representation capabilities.•We redesign MobileNet to effectively resolve the conflict between recognition performance and computational utilization. Meanwhile, transfer learning is successfully applied to the DBLCNN.•Experimental results show that the DBLCNN method achieves state-of-the-art recognition performance and excellent computational efficiency on the BreakHis dataset.