Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 29 April 2022 / Approved: 13 May 2022 / Online: 13 May 2022 (10:17:00 CEST)

How to cite: Blake, N.; Gaifulina, R.; Griffin, L.D.; Bell, I.M.; Thomas, G.M.H. Machine Learning of Raman Spectroscopy Data for Classifying Cancers: a Review of the Recent Literature. Preprints 2022, 2022050189 (doi: 10.20944/preprints202205.0189.v1). Blake, N.; Gaifulina, R.; Griffin, L.D.; Bell, I.M.; Thomas, G.M.H. Machine Learning of Raman Spectroscopy Data for Classifying Cancers: a Review of the Recent Literature. Preprints 2022, 2022050189 (doi: 10.20944/preprints202205.0189.v1).

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 its 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 upon 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.

Keywords

Raman Spectroscopy; Medical application; Disease screening and diagnosis; Machine learning analyses

Subject

MEDICINE & PHARMACOLOGY, Pathology & Pathobiology

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