Pap-smear Image Classification Using Randomized Neural Network Based Signature

  • Jarbas Joaci de Mesquita Sá Junior
  • André R. Backes
  • Odemir Martinez Bruno
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

Abstract

This paper presents a state-of-the-art texture analysis method called “randomized neural network based signature” applied to the classification of pap-smear cell images for the Papanicolaou test. For this purpose, we used a well-known benchmark dataset composed of 917 images and compared the aforementioned image signature to other texture analysis methods. The obtained results were promising, presenting accuracy of 87.57% and AUC of 0.8983 using LDA and SVM, respectively. These performance values confirm that the randomized neural network based signature can be applied successfully to this important medical problem.

Keywords

Randomized neural network Pap-smear database Texture analysis 

Notes

Acknowledgments

Jarbas Joaci de Mesquita Sá Junior thanks CNPq (National Council for Scientific and Technological Development, Brazil) (Grant: 152054/2016-2 and 453835/2017-1) for the financial support of this work. André R. Backes gratefully acknowledges the financial support of CNPq (Grant #302416/2015-3) and FAPEMIG (Foundation to the Support of Research in Minas Gerais) (Grant #APQ-03437-15). Odemir M. Bruno gratefully acknowledges the financial support of CNPq (307797/2014-7 and 484312/2013-8) and FAPESP (14/08026-1).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jarbas Joaci de Mesquita Sá Junior
    • 1
    • 2
  • André R. Backes
    • 3
  • Odemir Martinez Bruno
    • 1
  1. 1.São Carlos Institute of PhysicsUniversity of São PauloSão CarlosBrazil
  2. 2.Department of Computer EngineeringCampus de Sobral - Universidade Federal do CearáSobralBrazil
  3. 3.School of Computer ScienceUniversidade Federal de UberlândiaUberlândiaBrazil

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