A unified machine learning framework for oral cancer classification using Raman spectra from tissue, serum, and urine.

Researchers developed a machine learning framework using Raman spectroscopy on tissue, serum, and urine samples to classify oral cancer, with an SVM model achieving 96.4% diagnostic accuracy. The study demonstrated that spectral patterns differ across biological sample types but can still effectively distinguish between normal and cancerous samples.

A plain-English summary of the study's own findings — we don't add our own claims or interpretation. Read the full paper for the complete methods and results, and apply your own clinical judgement.