Grading of Melanoma Tissues by Raman MicroSpectroscopy - Gianmarco Lazzini; Mario D’Acunto (Engineering Proceedings)
Melanoma is one of the most aggressive forms of cancer. Early-stage diagnosis is therefore a landmark for the success of the therapies and to improve the prognosis. Raman spectroscopy represents a powerful and label-free approach for the molecular characterization of biological samples. Due to its level of detail, when applied to cancer tissues, Raman spectroscopy can help the classification of cancer-related malignant degrees. However, there is a high similarity between Raman spectra related to different cancerous tissues, which requires the use of sophisticated techniques for the treatment of Raman data. In this work, we coupled Confocal Raman Microscopy and Machine Learning techniques for the automatic classification of ex vivo melanoma tissues. In particular, we compared the performance of a PCA+LDA routine with a Random Forest Classifier. The work demonstrated excellent Machine Learning performances in classifying the tissues under investigation.