Cutaneous melanoma is an aggressive form of skin cancer and a leading cause of cancer-related mortality. In this sense, Raman Spectroscopy (RS) could represent a fast and effective method for melanoma-related diagnosis. We therefore introduced a newmethod based on RS to distinguish Compound Naevi (CN) from Primary Cutaneous Melanoma (PCM) from ex vivo solid biopsies. To this aim, integrating Confocal Raman Micro-Spectroscopy (CRM) with four Machine Learning (ML) algorithms: Linear Discrimi nant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), and Random Forest Classifier (RFC). We focused our attention on the comparison between traditional pre-processing operations with Continuous Wavelet Transform (CWT).
In particular, CWT led to the maximum classification accuracy, which was of ∼89.0%, which highlighted the method as promising in view of future implementations in devices for everyday use.