Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Campus de Tafira, 35017 Las Palmas, Spain.
Wessex Neurological Centre, University Hospital Southampton, Tremona Road, Southampton SO16 6YD, UK.
Sensors (Basel). 2018 Dec 18;18(12):4487. doi: 10.3390/s18124487.
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. 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.
本文的工作重点是使用光谱学通过支持向量机分类器来识别人类脑组织样本的类型。使用两种不同的光谱仪在波数范围为 1200⁻3500cm 之间获取红外光谱特征。进行了广泛的分析以找到支持向量机分类器的最佳配置，并确定光谱对于该特定应用最相关的区域。结果表明，所开发的算法足够稳健，可以在三个不同的判别水平上对人脑组织的红外光谱数据进行分类。
