Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/55016
Title: SVM optimization for brain tumor identification using infrared spectroscopic samples
Authors: Fabelo, Himar 
Ortega, Samuel 
Casselden, Elizabeth
Loh, Jane
Bulstrode, Harry
Zolnourian, Ardalan
Grundy, Paul
Callico, Gustavo M. 
Bulters, Diederik
Sarmiento, Roberto 
Keywords: Cancer-Detection
Classification
Diagnosis
Gliomas
Tissue, et al
Issue Date: 2018
Publisher: 1424-8220
Journal: Sensors 
Abstract: The work presented in this paper is focused on the use of spectroscopy to identify the type of tissue of human brain samples employing support vector machine classifiers. Two different spectrometers were used to acquire infrared spectroscopic signatures in the wavenumber range between 1200-3500 cm(-1). An extensive analysis was performed to find the optimal configuration for a support vector machine classifier and determine the most relevant regions of the spectra for this particular application. The results demonstrate that the developed algorithm is robust enough to classify the infrared spectroscopic data of human brain tissue at three different discrimination levels.
URI: http://hdl.handle.net/10553/55016
ISSN: 1424-8220
DOI: 10.3390/s18124487
Source: Sensors (Switzerland)[ISSN 1424-8220],v. 18 (4487)
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