Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/46806
Title: Acceleration of brain cancer detection algorithms during surgery procedures using GPUs
Authors: Torti, E.
Fontanella, A.
Florimbi, G.
Leporati, F.
Fabelo, H. 
Ortega, S. 
Callico, G. M. 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Brain cancer detection
European projects in digital systems design
GPU
SVMs
Issue Date: 2018
Publisher: 0141-9331
Journal: Microprocessors and Microsystems 
Conference: 20th Euromicro Conference on Digital System Design (DSD) 
Abstract: 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
Source: Microprocessors and Microsystems[ISSN 0141-9331],v. 61, p. 171-178
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