Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/53627

TitleBlood type classification using computer vision and machine learning
Author(s)Ferraz, Ana
Brito, José Henrique
Carvalho, Vítor
Machado, José
KeywordsBlood types
Pre-transfusion tests
Plate test
Image processing
Machine learning
Issue date2017
PublisherSpringer
JournalNeural Computing & Applications
Abstract(s)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.
TypeArticle
URIhttp://hdl.handle.net/1822/53627
DOI10.1007/s00521-015-2151-1
ISSN0941-0643
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CT2M - Artigos em revistas de circulação internacional com arbitragem científica

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