Simple machine learning methods work surprisingly well for Ramanomics

Celestine P. Lawrence*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Recently, a deep convolutional neural network was employed to detect liver cancer by Ramanomics. Results on a demo dataset claimed an accuracy of about 90% to be achieved after around an hour of training in a modern desktop. However, my experience with another Ramanomics dataset taught me that simple methods could potentially outperform deep learning. Here, I tested the simple and interpretable method of logistic regression. It achieved an accuracy of around 90.4% in under a minute. Employing a random decision forest, yields an accuracy of 92.6% in under 10 seconds. Thus, although deep learning is promising, it is yet to provide a quantum-leap in performance for Ramanomics. A biophysics aware machine learning method would be more welcome!
Original languageEnglish
Pages (from-to)887-889
Number of pages3
JournalJournal of Raman Spectroscopy
Volume54
Issue number8
Early online date10-May-2023
DOIs
Publication statusPublished - Aug-2023

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