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

被引:33
作者
Banbury, Carl [1 ]
Mason, Richard [2 ]
Styles, Iain [3 ]
Eisenstein, Neil [1 ]
Clancy, Michael [1 ]
Belli, Antonio [4 ]
Logan, Ann [4 ]
Oppenheimer, Pola Goldberg [1 ]
机构
[1] Univ Birmingham, Chem Engn, Birmingham, W Midlands, England
[2] Univ Birmingham, Phys & Astron, Birmingham, W Midlands, England
[3] Univ Birmingham, Comp Sci, Birmingham, W Midlands, England
[4] Univ Birmingham, Inst Inflammat & Ageing, Birmingham, W Midlands, England
基金
英国工程与自然科学研究理事会;
关键词
CLASSIFICATION;
D O I
10.1038/s41598-019-47205-5
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
070301 [无机化学]; 070403 [天体物理学]; 070507 [自然资源与国土空间规划学]; 090105 [作物生产系统与生态工程];
摘要
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.
引用
收藏
页数:9
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