Diagnosis of Cervical Cancer Using the Median M-Type Radial Basis Function (MMRBF) Neural Network

  • Authors:
  • Margarita E. Gómez-Mayorga;Francisco J. Gallegos-Funes;José M. De-La-Rosa-Vázquez;Rene Cruz-Santiago;Volodymyr Ponomaryov

  • Affiliations:
  • National Polytechnic Institute of Mexico, Interdisciplinary Professional Unit of Engineering and Advanced Technology,;Mechanical and Electrical Engineering Higher School, Av. IPN s/n, U.P.A.L.M. SEPI-ESIME, Edif. Z, Acceso 3, Tercer Piso, Col. Lindavista, Mexico, Mexico 07738;Mechanical and Electrical Engineering Higher School, Av. IPN s/n, U.P.A.L.M. SEPI-ESIME, Edif. Z, Acceso 3, Tercer Piso, Col. Lindavista, Mexico, Mexico 07738;Mechanical and Electrical Engineering Higher School, Av. IPN s/n, U.P.A.L.M. SEPI-ESIME, Edif. Z, Acceso 3, Tercer Piso, Col. Lindavista, Mexico, Mexico 07738;Mechanical and Electrical Engineering Higher School, Av. IPN s/n, U.P.A.L.M. SEPI-ESIME, Edif. Z, Acceso 3, Tercer Piso, Col. Lindavista, Mexico, Mexico 07738

  • Venue:
  • MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
  • Year:
  • 2009

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Abstract

The automatic analysis of Pap smear microscopic images is one of the most interesting fields in biomedical image processing. In this paper we present the capability of the Median M-Type Radial Basis Function (MMRBF) neural network in the classification of cervical cancer cells. From simulation results we observe that the MMRBF neural network has better classification capabilities in comparison with the Median RBF algorithm used as comparative.