Performance comparison of machine learning methods for prognosis of hormone receptor status in breast cancer tissue samples

Sarıkoç F., KALINLI A., AKGÜN H., ÖZTÜRK F.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol.110, no.3, pp.298-307, 2013 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 110 Issue: 3
  • Publication Date: 2013
  • Doi Number: 10.1016/j.cmpb.2012.12.005
  • Journal Name: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.298-307
  • Keywords: Image processing, Classification, Nucleus segmentation, Estrogen receptor (ER) status evaluation, Breast cancer prognosis, ESTROGEN-RECEPTOR, IMAGE-ANALYSIS, IMMUNOHISTOCHEMISTRY, QUANTIFICATION, MICROARRAYS, CARCINOMAS, EXPRESSION, SECTIONS, SYSTEM, ISSUES
  • Middle East Technical University Affiliated: No

Abstract

We examined the classification and prognostic scoring performances of several computer methods on different feature sets to obtain objective and reproducible analysis of estrogen receptor status in breast cancer tissue samples.