Work

Raman Spectroscopy Diagnosis of Melanoma

Year: 2025

Type: conference-paper

Abstract: 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 new method 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 Discriminant 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 ∼89.0%, which highlighted the method as promising in view of future implementations in devices for everyday use.

DOI: 10.3390/proceedings2025129010

Authors: Gianmarco Lazzini, Daniela Massi, Davide Moroni, Ovidio Salvetti, Paolo Viacava, Marco Laurino, Mario D’Acunto

Institutions: Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo", National Research Council, University of Florence, Ospedale di Livorno, Azienda USL Toscana Nord Ovest, Istituto di Fisiologia Clinica

Language: English

Topic: Spectroscopy Techniques in Biomedical and Chemical Research

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

OpenAlex ID: W4414262242

FWCI:

Cites: 7

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