Machine Learning for Raman Spectroscopy Glioblastoma Classification

Abstract

This study presents a comprehensive evaluation of various computational models for glioblastoma cell classification using Raman spectroscopy data. We compare traditional machine learning methods such as support vector machines, boosting and random forests with more modern methods including convolutional neural networks, vision transformers and the broad learning system. We find that convolutional neural networks are the most successful model for this data set and perform best when some feature normalisation is performed but without preprocessing methods such as background drift removal. We also find that data augmentation did not improve performance which is contrary to other published work in the area.