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

Research output: Contribution to journalArticlepeer-review

External organisations

  • School of Chemical Engineering, University of Birmingham, Birmingham, United Kingdom.
  • School of Physics and Astronomy, University of Birmingham, Birmingham, UK
  • School of Computer Science, University of Birmingham, Birmingham, United Kingdom.
  • Institute of Inflammation and Ageing, Centre for Translational Inflammation Research, University of Birmingham, Edgbaston, Birmingham, UK.
  • Chemical Engineering, University of Birmingham, Birmingham, UK.


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 types. Unlike traditional packages, the method performs data analysis directly in the web browser through modern web and cloud technologies as an open source extendable web app. The unprecedented accuracy and clarity of the SKiNET methodology has the potential to revolutionise the use of Raman spectroscopy for in-vivo applications.


Original languageEnglish
Article number10812
JournalScientific Reports
Issue number1
Publication statusPublished - 25 Jul 2019