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Neural Computing and Applications
Published in: Neural Computing and Applications 8/2017

13-01-2016 | Original Article

Blood type classification using computer vision and machine learning

Authors: Ana Ferraz, José Henrique Brito, Vítor Carvalho, José Machado

Published in: Neural Computing and Applications | Issue 8/2017

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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.
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Metadata
Title
Blood type classification using computer vision and machine learning
Authors
Ana Ferraz
José Henrique Brito
Vítor Carvalho
José Machado
Publication date
13-01-2016
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 8/2017
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-2151-1

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