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Naik, N., Madani, A., Esteva, A., Keskar, N.S., Press, M.F., Ruderman, D., Agus, D.B. and Socher, R. (2020) Deep Learning-Enabled Breast Cancer Hormonal Receptor Status Determination from Base-Level H&E Stains. Nature Communications, 11, Article No. 5727.
https://doi.org/10.1038/s41467-020-19334-3

has been cited by the following article:

• TITLE: A Novel Method for Diagnosis of Breast Cancer Tumors Based on Random Forest

  AUTHORS: Mengying Cai

  KEYWORDS: Random Forest, Breast Cancer Diagnosis, Machine Learning

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.11 No.4, April 24, 2023

  ABSTRACT: GLOBOCAN 2020 cancer data shows that female breast cancer has become the most common cancer over lung cancer for the first time. As a disease threatening the life safety of women all over the world, how to improve the accuracy of breast cancer diagnosis and help patients get treatment as early as possible is of great importance. This paper introduces a new random forest-based breast cancer diagnosis method (NRFM), using the average radius, average texture, average circumference and other 30 indicators in the nucleus of breast mass as characteristics, to diagnose the benign and malignant breast cancer. NRFM proposed to randomly miss a certain percentage of breast cancer data, using random forest regression to fill in the experiment proved that using the method proposed in this paper, when the proportion of missing data reached 50%, the accuracy of breast cancer diagnosis will be as high as 96.85%. Experiments show that NRFM is easy to understand, convenient to operate, and has practical application value, which can assist doctors to improve the accuracy of breast cancer diagnosis.