Raman Spectroscopy and Machine Learning in the Diagnosis of Breast Cancer.

ABSTRACT: The most prevalent cancer in women worldwide, breast cancer, greatly benefits from early identification for better prognoses. But traditional diagnostic techniques, like biopsies and mammograms, can require invasive procedures and lack accuracy. The non-invasive, quick, and accurate nature of machine learning (ML) and Raman spectroscopy (RS) in breast cancer diagnoses are examined in this review. Combining machine learning's capacity to analyse intricate spectrum datasets with Raman spectroscopy's ability to produce molecular fingerprints of biochemical alterations linked to cancer improves diagnostic precision. Using the PRISMA methodology, studies published from 2017 to 2024 were examined, with an emphasis on those that reported sensitivity and specificity values greater than 80%. With s

SUBMITTER: Rao S 

PROVIDER: S-EPMC12405035 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature