Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 796387, 17 pages
Research Article

Development of an Expert System as a Diagnostic Support of Cervical Cancer in Atypical Glandular Cells, Based on Fuzzy Logics and Image Interpretation

1Instituto Tecnológico de Orizaba, Avenida Oriente 9 No. 852, Colonia Emiliano Zapata, 94320 Orizaba, VER, Mexico
2Hospital Regional de Río Blanco, Entronque Autopista Orizaba-Puebla km 2, 94735 Río Blanco, VER, Mexico

Received 26 October 2012; Revised 16 January 2013; Accepted 17 February 2013

Academic Editor: Alejandro Rodríguez González

Copyright © 2013 Karem R. Domínguez Hernández et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Cervical cancer is the second largest cause of death among women worldwide. Nowadays, this disease is preventable and curable at low cost and low risk when an accurate diagnosis is done in due time, since it is the neoplasm with the highest prevention potential. This work describes the development of an expert system able to provide a diagnosis to cervical neoplasia (CN) precursor injuries through the integration of fuzzy logics and image interpretation techniques. The key contribution of this research focuses on atypical cases, specifically on atypical glandular cells (AGC). The expert system consists of 3 phases: (1) risk diagnosis which consists of the interpretation of a patient’s clinical background and the risks for contracting CN according to specialists; (2) cytology images detection which consists of image interpretation (IM) and the Bethesda system for cytology interpretation, and (3) determination of cancer precursor injuries which consists of in retrieving the information from the prior phases and integrating the expert system by means of a fuzzy logics (FL) model. During the validation stage of the system, 21 already diagnosed cases were tested with a positive correlation in which 100% effectiveness was obtained. The main contribution of this work relies on the reduction of false positives and false negatives by providing a more accurate diagnosis for CN.