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Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature

Blake, Nathan; Gaifulina, Riana; Griffin, Lewis D; Bell, Ian M; Thomas, Geraint MH; (2022) Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature. Diagnostics , 12 (6) , Article 1491. 10.3390/diagnostics12061491. Green open access

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Abstract

Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models.

Type: Article
Title: Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/diagnostics12061491
Publisher version: https://doi.org/10.3390/diagnostics12061491
Language: English
Additional information: © 2022 MDPI. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
Keywords: Raman Spectroscopy; medical application; disease screening and diagnosis; machine learning; cross-validation; deep learning
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Cell and Developmental Biology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
URI: https://discovery.ucl.ac.uk/id/eprint/10150813
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