DBLCNN: Dependency-based lightweight convolutional neural network for multi-classification of breast histopathology images. (March 2022)

• Record Type: Journal Article
• Authors: Wang, Chaoqing; Gong, Weijun; Cheng, Junlong; Qian, Yurong
• Is Part Of: Biomedical signal processing and control. Volume 73(2022)
• Publication Date: 2022-03
• Subjects: Deep learning -- Image classification -- Computer vision -- Medical imaging -- Breast cancer; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103451
• Languages: English
• ISSNs: 1746-8094

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. Abstract: 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-the-art effects in terms of recognition performance and computational utilization.