SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples.
Published version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Fabelo, Himar
Ortega, Samuel
Casselden, Elizabeth
Loh, Jane
Bulstrode, Harry https://orcid.org/0000-0002-3480-108X
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.
Description
Keywords
brain cancer, medical imaging, spectroscopy, support vector machines, tissue diagnostics, Brain Neoplasms, Humans, Sensitivity and Specificity, Spectrophotometry, Infrared, Support Vector Machine
Journal Title
Sensors
Conference Name
Journal ISSN
1424-8220
1424-8220
1424-8220
Volume Title
18
Publisher
MDPI
Publisher DOI
Sponsorship
This work has been supported in part by the European Commission through the FP7 FET Open
Programme ICT-2011.9.2, European Project HELICoiD “HypErspectral Imaging Cancer Detection” under Grant
Agreement 618080. This work has been also supported in part by the Canary Islands Government through the
ACIISI (Canarian Agency for Research, Innovation and the Information Society), ITHACA project “Hyperespectral
identification of Brain tumors” under Grant Agreement ProID2017010164. Additionally, this work has been
supported in part by the 2016 PhD Training Program for Research Staff of the University of Las Palmas de Gran
Canaria. Finally, this work was completed while Samuel Ortega was beneficiary of a pre-doctoral grant given by
the “Agencia Canaria de Investigacion, Innovacion y Sociedad de la Información (ACIISI)” of the “Conserjería de Economía,
Industria, Comercio y Conocimiento” of the “Gobierno de Canarias”, which is part-financed by the European Social
Fund (FSE) (POC 2014-2020, Eje 3 Tema Prioritario 74 (85%)).