Tissue characterization using a multi-pixel sensor system and partial least squares regression
Publication
, Conference
Woods, CM; Senlik, O; Jokerst, NM
Published in: Optics InfoBase Conference Papers
January 1, 2020
Diffuse reflectance spectroscopy systems often exhibit low SNR, reducing tissue characterization accuracy. We present a new analysis showing lower SNR requirements when partial least squares regression is used for prediction instead of more traditional techniques.
Duke Scholars
Published In
Optics InfoBase Conference Papers
EISSN
2162-2701
Publication Date
January 1, 2020
Citation
APA
Chicago
ICMJE
MLA
NLM
Woods, C. M., Senlik, O., & Jokerst, N. M. (2020). Tissue characterization using a multi-pixel sensor system and partial least squares regression. In Optics InfoBase Conference Papers.
Woods, C. M., O. Senlik, and N. M. Jokerst. “Tissue characterization using a multi-pixel sensor system and partial least squares regression.” In Optics InfoBase Conference Papers, 2020.
Woods CM, Senlik O, Jokerst NM. Tissue characterization using a multi-pixel sensor system and partial least squares regression. In: Optics InfoBase Conference Papers. 2020.
Woods, C. M., et al. “Tissue characterization using a multi-pixel sensor system and partial least squares regression.” Optics InfoBase Conference Papers, 2020.
Woods CM, Senlik O, Jokerst NM. Tissue characterization using a multi-pixel sensor system and partial least squares regression. Optics InfoBase Conference Papers. 2020.
Published In
Optics InfoBase Conference Papers
EISSN
2162-2701
Publication Date
January 1, 2020