Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/46806
Título: Acceleration of brain cancer detection algorithms during surgery procedures using GPUs
Autores/as: Torti, E.
Fontanella, A.
Florimbi, G.
Leporati, F.
Fabelo, H. 
Ortega, S. 
Callico, G. M. 
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Brain cancer detection
European projects in digital systems design
GPU
SVMs
Fecha de publicación: 2018
Editor/a: 0141-9331
Publicación seriada: Microprocessors and Microsystems 
Conferencia: 20th Euromicro Conference on Digital System Design (DSD) 
Resumen: The HypErspectraL Imaging Cancer Detection (HELICoiD) European project aims at developing a methodology for tumor tissue classification through hyperspectral imaging (HSI) techniques. This paper describes the development of a parallel implementation of the Support Vector Machines (SVMs) algorithm employed for the classification of hyperspectral (HS) images of in vivo human brain tissue. SVM has demonstrated high accuracy in the supervised classification of biological tissues, and especially in the classification of human brain tumor. In this work, both the training and the classification stages of the SVMs were accelerated using Graphics Processing Units (GPUs). The acceleration of the training stage allows incorporating new samples during the surgical procedures to create new mathematical models of the classifier. Results show that the developed system is capable to perform efficient training and real-time compliant classification.
URI: http://hdl.handle.net/10553/46806
ISSN: 0141-9331
DOI: 10.1016/j.micpro.2018.06.005
Fuente: Microprocessors and Microsystems[ISSN 0141-9331],v. 61, p. 171-178
Colección:Artículos
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