A Deep Learning Method for Breast Cancer Classification in the Pathology Images

Min Liu, Lanlan Hu, Ying Tang, Chu Wang, Yu He, Chunyan Zeng, Kun Lin, Zhizi He, Wujie Huo

Research output: Contribution to journalArticlepeer-review

53 Scopus citations


Objective: Breast cancer is the most common female cancer in the world, and it poses a huge threat to women's health. There is currently promising research concerning its early diagnosis using deep learning methodologies. However, some commonly used Convolutional Neural Network (CNN) and their variations, such as AlexNet, VGGNet, GoogleNet and so on, are prone to overfitting in breast cancer classification, due to both small-scale breast pathology image datasets and overconfident softmax-cross-entropy loss. To alleviate the overfitting issue for better classification accuracy, we propose a novel framework for breast pathology classification, called the AlexNet-BC model. The model is pre-trained using the ImageNet dataset and fine-tuned using an augmented dataset. We also devise an improved cross-entropy loss function to penalize overconfident low-entropy output distributions and make the predictions suitable for uniform distributions. The proposed approach is then validated through a series of comparative experiments on BreaKHis, IDC and UCSB datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods at different magnifications. Its strong robustness and generalization capabilities make it suitable for histopathology clinical computer-aided diagnosis systems.

Original languageEnglish (US)
Pages (from-to)5025-5032
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Issue number10
StatePublished - Oct 1 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management


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