Breast Cancer Classi Cation through Mixture of Bivariate Normal Using EM Algorithm

Authors

  • Gerardo Martínez-Guzmán Benemérita Universidad Autónoma de Puebla
  • Carmen Cerón-Garnica Benemérita Universidad Autónoma de Puebla
  • Jorge Alejandro Fernández-Pérez Benemérita Universidad Autónoma de Puebla
  • Gerardo Villegas-Cerón Benemérita Universidad Autónoma de Puebla

DOI:

https://doi.org/10.13053/cys-28-4-5296

Keywords:

Maximum likelihood estimators, breast cancer, EM algorithm, Gaussian mixture model

Abstract

The increase of nuclear size is observed in biopsies of patients with benign and malignant diagnosis, as well as, a change in the texture of the nucleus. In this article, an analysis of the variables radius mean (mean of distances from center to points on the perimeter) and texture (standard deviation of gray-scale values) is made using the unsupervised learning algorithm, Ex-pectation-Maximization (EM). Since we observe that those variables have a similar behavior to the mixture of normals in two components. Such algorithm is able to discriminate the data into two groups (malignant and benign). Said model projects a classi cation with a high percentage of coincidence with the observed data.

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Published

2024-12-03

Issue

Vol. 28 No. 4 (2024): 28(4) 2024

Section

Articles of the Thematic Section

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