Please use this identifier to cite or link to this item:
http://hdl.handle.net/10553/77072
Title: | Hyperspectral Imaging for Major Neurocognitive Disorder Detection in Plasma Samples | Authors: | León Martín, Sonia Raquel Martínez Vega, Beatriz Fabelo Gómez, Himar Antonio Ortega Sarmiento, Samuel Marrero Callicó, Gustavo Iván Balea Fernández, Francisco Javier Bilbao Sieyro, Cristina |
UNESCO Clasification: | 32 Ciencias médicas 320507 Neurología 320711 Neuropatología |
Keywords: | Hyperspectral Imaging K-Nearest Neighborhood Neurocognitive Disorders Random Forest Supervised Learning, et al |
Issue Date: | 2020 | Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | Conference: | 35th Conference on Design of Circuits and Integrated Systems - DCIS 2020 | Abstract: | Neurocognitive disorders (NCD) affect over 50 million people globally. The detection biomarkers using brain imaging or cerebrospinal fluid are expensive procedures. Blood-based biomarkers such as plasma or serum present a cost-effective alternative. The work presented in this paper is focused on the use of hyperspectral (HS) imaging (HSI) to classify plasma samples in order to discriminate between patients with major NCD and healthy control subjects. HS images of plasma samples were obtained using a SWIR (Short-Wave Infrared) camera able to capture 273 bands within the 900-2,500 nm spectral range. A preliminary HSI database was obtained with 20 major NCD samples and 20 control samples. This data was segmented and classified using pixel-wise supervised classification algorithms, achieving 75% sensitivity and 100% specificity results with the best classifier in the test set. | URI: | http://hdl.handle.net/10553/77072 | ISBN: | 9781728191324 | DOI: | 10.1109/DCIS51330.2020.9268625 | Source: | 2020 XXXV Conference on Design of Circuits and Integrated Systems (DCIS) |
Appears in Collections: | Actas de congresos |
Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.