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Neural Computing and Applications

, Volume 28, Issue 8, pp 2029–2040 | Cite as

Blood type classification using computer vision and machine learning

  • Ana Ferraz
  • José Henrique Brito
  • Vítor Carvalho
  • José MachadoEmail author
Original Article

Abstract

In emergency situations, where time for blood transfusion is reduced, the O negative blood type (the universal donor) is administrated. However, sometimes even the universal donor can cause transfusion reactions that can be fatal to the patient. As commercial systems do not allow fast results and are not suitable for emergency situations, this paper presents the steps considered for the development and validation of a prototype, able to determine blood type compatibilities, even in emergency situations. Thus it is possible, using the developed system, to administer a compatible blood type, since the first blood unit transfused. In order to increase the system’s reliability, this prototype uses different approaches to classify blood types, the first of which is based on Decision Trees and the second one based on support vector machines. The features used to evaluate these classifiers are the standard deviation values, histogram, Histogram of Oriented Gradients and fast Fourier transform, computed on different regions of interest. The main characteristics of the presented prototype are small size, lightweight, easy transportation, ease of use, fast results, high reliability and low cost. These features are perfectly suited for emergency scenarios, where the prototype is expected to be used.

Keywords

Blood types Pre-transfusion tests Plate test Image processing Machine learning 

Notes

Acknowledgments

Authors of this paper want to thank Portuguese Foundation for Science and Technology (FCT) for funding through the PhD scholarship SFRH/BD/81094/2011. This work is funded also by FEDER funds through the “Programa Operacional Factores de Competitividade—COMPETE” and by national funds by FCTFundação para a Ciência e a Tecnologia, project reference PEst-UID/CEC/00319/2013.

Compliance with ethical standards

Conflict of interest

The authors declare that they have not conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Ana Ferraz
    • 1
    • 2
  • José Henrique Brito
    • 3
  • Vítor Carvalho
    • 1
    • 3
  • José Machado
    • 2
    Email author
  1. 1.R&D ALGORITMI Center, School of EngineeringUniversity of MinhoGuimarãesPortugal
  2. 2.MEtRICs Research Center, School of EngineeringUniversity of MinhoGuimarãesPortugal
  3. 3.IPCA-ESTPolytechnic Institute of Cávado and AveBarcelosPortugal

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