SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples
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⁻...
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MDPI AG
2018-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/12/4487 |
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doaj-20054eeb09c04477964606495e88dce22020-11-25T01:02:25ZengMDPI AGSensors1424-82202018-12-011812448710.3390/s18124487s18124487SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic SamplesHimar Fabelo0Samuel Ortega1Elizabeth Casselden2Jane Loh3Harry Bulstrode4Ardalan Zolnourian5Paul Grundy6Gustavo M. Callico7Diederik Bulters8Roberto Sarmiento9Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Campus de Tafira, Las Palmas 35017, SpainInstitute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Campus de Tafira, Las Palmas 35017, SpainWessex Neurological Centre, University Hospital Southampton, Tremona Road, Southampton SO16 6YD, UKWessex Neurological Centre, University Hospital Southampton, Tremona Road, Southampton SO16 6YD, UKDepartment of Neurosurgery, Addenbrookes Hospital and University of Cambridge, Cambridge CB2 0QQ, UKWessex Neurological Centre, University Hospital Southampton, Tremona Road, Southampton SO16 6YD, UKWessex Neurological Centre, University Hospital Southampton, Tremona Road, Southampton SO16 6YD, UKInstitute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Campus de Tafira, Las Palmas 35017, SpainWessex Neurological Centre, University Hospital Southampton, Tremona Road, Southampton SO16 6YD, UKInstitute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Campus de Tafira, Las Palmas 35017, SpainThe 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<sup>−1</sup>. 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.https://www.mdpi.com/1424-8220/18/12/4487spectroscopytissue diagnosticsmedical imagingsupport vector machinesbrain cancer |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Himar Fabelo Samuel Ortega Elizabeth Casselden Jane Loh Harry Bulstrode Ardalan Zolnourian Paul Grundy Gustavo M. Callico Diederik Bulters Roberto Sarmiento |
| spellingShingle |
Himar Fabelo Samuel Ortega Elizabeth Casselden Jane Loh Harry Bulstrode Ardalan Zolnourian Paul Grundy Gustavo M. Callico Diederik Bulters Roberto Sarmiento SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples Sensors spectroscopy tissue diagnostics medical imaging support vector machines brain cancer |
| author_facet |
Himar Fabelo Samuel Ortega Elizabeth Casselden Jane Loh Harry Bulstrode Ardalan Zolnourian Paul Grundy Gustavo M. Callico Diederik Bulters Roberto Sarmiento |
| author_sort |
Himar Fabelo |
| title |
SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples |
| title_short |
SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples |
| title_full |
SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples |
| title_fullStr |
SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples |
| title_full_unstemmed |
SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples |
| title_sort |
svm optimization for brain tumor identification using infrared spectroscopic samples |
| publisher |
MDPI AG |
| series |
Sensors |
| issn |
1424-8220 |
| publishDate |
2018-12-01 |
| description |
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<sup>−1</sup>. 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. |
| topic |
spectroscopy tissue diagnostics medical imaging support vector machines brain cancer |
| url |
https://www.mdpi.com/1424-8220/18/12/4487 |
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