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Using Random Forest to Classify Raman Spectra of Brain Tissues


Traditional diagnosis of brain tumors is performed by neurologic exams and relies on specialists. The key difference between normal tissues and brain tumors can also be reflected through their Raman spectrum, which provides a fingerprint to identify different matters. In this thesis, we present an integral process from raw data pre-processing to model conducting and evaluation for identifying the white matter, grey matter and blood vessels. The mock spectra and several machine learning algorithms were used for choosing the configuration for the pipeline. The result shows the good prediction and stability of our approach in discriminating these three types of spectra with high accuracy.

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