Development of the Self Optimising Kohonen Index Network (SKiNET) for Raman Spectroscopy Based Detection of Anatomical Eye Tissue.

ABSTRACT: Raman spectroscopy shows promise as a tool for timely diagnostics via in-vivo spectroscopy of the eye, for a number of ophthalmic diseases. By measuring the inelastic scattering of light, Raman spectroscopy is able to reveal detailed chemical characteristics, but is an inherently weak effect resulting in noisy complex signal, which is often difficult to analyse. Here, we embraced that noise to develop the self-optimising Kohonen index network (SKiNET), and provide a generic framework for multivariate analysis that simultaneously provides dimensionality reduction, feature extraction and multi-class classification as part of a seamless interface. The method was tested by classification of anatomical ex-vivo eye tissue segments from porcine eyes, yielding an accuracy >93% across 5 tissue type

SUBMITTER: Banbury C 

PROVIDER: S-EPMC6658481 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature