Raman Spectroscopy Integrated with Artificial Intelligence for Advanced Cancer Diagnosis

Abstract

Getting an accurate and early diagnosis of cancer is still a big problem because traditional diagnostic methods are often invasive and don't target specific molecules. This study introduces a comprehensive methodology utilizing Raman spectroscopy and artificial intelligence (AI) to enhance cancer detection and diagnosis. Raman spectroscopy is a non-invasive, label-free way to look at the biochemical makeup of biological tissues by using molecular fingerprinting. This lets researchers find changes in nucleic acids, proteins, and lipids that are linked to cancer or live tissues affected by cancer.
The study delineates the essential principles of Raman scattering and the arrangement of clinically relevant systems, encompassing fiber-optic probes for in vivo measurements. An AI-based analysis framework is used to improve diagnostic performance by using spectral preprocessing, feature extraction, and machine learning classification. The results exhibit elevated sensitivity and specificity across various cancer types, signifying the dependability of the suggested methodology. It's possible uses in real-time diagnosis and treatment monitoring also show how important it is for improving non-invasive cancer diagnostics.