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Sensors 2018, 18(12), 4487; https://doi.org/10.3390/s18124487

SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples

1
Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Campus de Tafira, Las Palmas 35017, Spain
2
Wessex Neurological Centre, University Hospital Southampton, Tremona Road, Southampton SO16 6YD, UK
3
Department of Neurosurgery, Addenbrookes Hospital and University of Cambridge, Cambridge CB2 0QQ, UK
*
Author to whom correspondence should be addressed.
Received: 24 November 2018 / Revised: 14 December 2018 / Accepted: 15 December 2018 / Published: 18 December 2018
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications)
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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. View Full-Text
Keywords: spectroscopy; tissue diagnostics; medical imaging; support vector machines; brain cancer spectroscopy; tissue diagnostics; medical imaging; support vector machines; brain cancer
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Fabelo, H.; Ortega, S.; Casselden, E.; Loh, J.; Bulstrode, H.; Zolnourian, A.; Grundy, P.; M. Callico, G.; Bulters, D.; Sarmiento, R. SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples. Sensors 2018, 18, 4487.

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