Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks

  • Miguel A. Molina-Cabello
  • Ezequiel López-Rubio
  • Rafael M. Luque-Baena
  • María Jesús Rodríguez-Espinosa
  • Karl Thurnhofer-Hemsi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)

Abstract

The detection of red blood cells in blood samples can be crucial for the disease detection in its early stages. The use of image processing techniques can accelerate and improve the effectiveness and efficiency of this detection. In this work, the use of the Circle Hough transform for cell detection and artificial neural networks for their identification as a red blood cell is proposed. Specifically, the application of neural networks (MLP) as a standard classification technique with (MLP) is compared with new proposals related to deep learning such as convolutional neural networks (CNNs). The different experiments carried out reveal the high classification ratio and show promising results after the application of the CNNs.

Keywords

Blood cell detection Blood cell classification Circle hough transform Convolutional neural networks 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Miguel A. Molina-Cabello
    • 1
  • Ezequiel López-Rubio
    • 1
  • Rafael M. Luque-Baena
    • 1
  • María Jesús Rodríguez-Espinosa
    • 1
  • Karl Thurnhofer-Hemsi
    • 1
  1. 1.Department of Computer Languages and Computer ScienceUniversity of MálagaMálagaSpain

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