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The HELICoiD Project: Parallel SVM for Brain Cancer Classification
- Source :
- DSD
- Publication Year :
- 2017
- Publisher :
- IEEE, 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).
- Subjects :
- Computer science
business.industry
Emerging technologies
0211 other engineering and technologies
Executive agency
02 engineering and technology
Cancer detection
Machine learning
computer.software_genre
01 natural sciences
Tumor tissue
Brain cancer
010309 optics
Support vector machine
Identification (information)
ComputingMethodologies_PATTERNRECOGNITION
0103 physical sciences
media_common.cataloged_instance
Artificial intelligence
European union
business
computer
021101 geological & geomatics engineering
media_common
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2017 Euromicro Conference on Digital System Design (DSD)
- Accession number :
- edsair.doi...........1ae05046adf160bd82b872e534361bfe