Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/53627
Título: | Blood type classification using computer vision and machine learning |
Autor(es): | Ferraz, Ana Brito, José Henrique Carvalho, Vítor Machado, José |
Palavras-chave: | Blood types Pre-transfusion tests Plate test Image processing Machine learning |
Data: | 2017 |
Editora: | Springer |
Revista: | Neural Computing and Applications |
Resumo(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. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/53627 |
DOI: | 10.1007/s00521-015-2151-1 |
ISSN: | 0941-0643 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
Aparece nas coleções: | CT2M - Artigos em revistas de circulação internacional com arbitragem científica |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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RI_WoS_15.pdf Acesso restrito! | 3,5 MB | Adobe PDF | Ver/Abrir |