Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/46810
Title: The HELICoiD Project: Parallel SVM for Brain Cancer Classification
Authors: Torti, Emanuele
Cividini, Camilla
Gatti, Alessandro
Danese, Giovanni
Leporati, Francesco
Fabelo, Himar 
Ortega, Samuel 
Callicò, Gustavo Marrero 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Hyperspectral imaging
Support vector machines
Tumors
Surgery
Cancer
Issue Date: 2017
Journal: 2017 Euromicro Conference On Digital System Design (Dsd)
Conference: 20th Euromicro Conference on Digital System Design, DSD 2017 
Abstract: This paper describes the challenge of real-time tumor tissue identification dealt with by the HypErspectraL Imaging Cancer Detection (HELICoiD) European project. This project was funded by the Research Executive Agency, through the Future and Emerging Technologies (FET-Open) programme, under the 7th Framework Programme of the European Union. It involved four universities, three industrial partners and two hospitals. In this paper, we focused on the activity performed by the University of Las Palmas de Gran Canaria, in collaboration with the University of Pavia, concerning the parallel implementation of Support Vector Machine (SVM) classification for tumor tissue identification during surgery. Obtained results show that this classification is real-time compliant when performed using Graphic Processing Units (GPUs).
URI: http://hdl.handle.net/10553/46810
ISBN: 9781538621455
DOI: 10.1109/DSD.2017.33
Source: Proceedings - 20th Euromicro Conference on Digital System Design, DSD 2017 (8049823), p. 445-450
Appears in Collections:Actas de congresos
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