Finding reduced Raman spectroscopy fingerprint of skin samples for melanoma diagnosis through machine learning

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dc.contributor.authorAraújo, Daniella Castro
dc.contributor.authorVeloso, Adriano Alonso
dc.contributor.authorOliveira Filho, Renato Santos de
dc.contributor.authorGiraud, Marie-Noelle
dc.contributor.authorRaniero, Leandro José
dc.contributor.authorFerreira, Lydia Masako
dc.contributor.authorBitar, Renata Andrade
dc.date.accessioned2025-06-13T11:24:26Z
dc.date.available2025-06-13T11:24:26Z
dc.date.issued22021
dc.description.abstractEarly-stage detection of cutaneous melanoma can vastly increase the chances of cure. Excision biopsy followed by histological examination is considered the gold standard for diagnosing the disease, but requires long high- cost processing time, and may be biased, as it involves qualitative assessment by a professional. In this paper, we present a new machine learning approach using raw data for skin Raman spectra as input. The approach is highly efficient for classifying benign versus malignant skin lesions (AUC 0.98, 95% CI 0.97–0.99). Furthermore, we present a high-performance model (AUC 0.97, 95% CI 0.95–0.98) using a miniaturized spectral range (896–1039 cm− 1), thus demonstrating that only a single fragment of the biological fingerprint Raman region is needed for producing an accurate diagnosis. These findings could favor the future development of a cheaper and dedicated Raman spectrometer for fast and accurate cancer diagnosis.
dc.description.physical9 p.
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.format.mimetypePDF
dc.identifier.affiliationUniversidade do Vale do Paraíba
dc.identifier.affiliationUniversidade Federal de Minas Gerais
dc.identifier.affiliationUniversidade Federal de São Paulo
dc.identifier.affiliationUniversity of Fribourg
dc.identifier.bibliographicCitationARAÚJO, D. C. et al. Finding reduced Raman spectroscopy fingerprint of skin samples for melanoma diagnosis through machine learning. Artificial Intelligence in Medicine, v. 120, p. 1-9, 2021. Disponível em: https://linkinghub.elsevier.com/retrieve/pii/S0933365721001548.
dc.identifier.doi10.1016/j.artmed.2021.102161
dc.identifier.urihttps://repositorio.univap.br/handle/123456789/1000
dc.language.isoen_US
dc.publisherElsevier
dc.rights.holderArtificial Intelligence in Medicine
dc.subject.keywordMachine learning
dc.subject.keywordMelanoma
dc.subject.keywordRaman spectroscopy
dc.subject.keywordOptical diagnosis
dc.subject.keywordExplanatory modeling
dc.titleFinding reduced Raman spectroscopy fingerprint of skin samples for melanoma diagnosis through machine learning
dc.typeArtigos de Periódicos

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