Home // COGNITIVE 2014, The Sixth International Conference on Advanced Cognitive Technologies and Applications // View article

Automatic Classification of Cells Patterns for Triple Negative Breast Cancer Identification

Authors:
Juan Luis Fernandez-Martınez
Ana Cernea
Enrique J. de Andres-Galiana
Primitiva Menendez-Rodriguez
Jose A. Galvan
Carmen Garcıa-Pravia

Keywords: Artificial Intelligence; Cognition; Cells Patterns; Triple Negative Breast Cancers (TNBC); Machine Learning

Abstract:
This paper is devoted to present a methodology in Artificial Intelligence and Cognition for the optimization of basal cells pattern classification. Different unsupervised and supervised learning techniques are applied to the analysis, diagnosis and prognosis of cell patterns classification for Triple Negative Breast Cancers (TNBC), a group of cancers that share with basal like breast cancer very bad prognosis. For that purpose, different machine learning algorithms are performed on histological images, and on a list of pathological and immunohistochemical variables currently-used in medical practice. The main objective is to design a biomedical robot able to assess physicians on the kind of histological grade of different subgroups of TNBC samples in order to optimize the treatment protocol. The proposed methodology is performed over a database of 116 patients. The results show that pathological and immunohistochemical variables and histological images provide complementary information to improve the classification of TNBC samples.

Pages: 151 to 158

Copyright: Copyright (c) IARIA, 2014

Publication date: May 25, 2014

Published in: conference

ISSN: 2308-4197

ISBN: 978-1-61208-340-7

Location: Venice, Italy

Dates: from May 25, 2014 to May 29, 2014