Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{171045,
        author = {Mrs Pallavi Badarala and Dr V Bhaskara raju and Ms Tejaswi Geddada and Ms V S A A SRI VYSHNAVI SUDARSANAM and Ms Rapolu Lakshmi sowjanya and Ms Gudala Krishna Goutami},
        title = {A REVIEW ON ARTIFICIAL INTELLIGENCE APPLIED ON RAMAN SPECTROSCOPY FOR MULTITUDINOUS CANCERS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {2801-2807},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171045},
        abstract = {Raman spectroscopy is a label-free optical method that has been used extensively for tumor diagnosis. Using conventional diagnostic techniques, tumors can be categorized as benign, malignant, or subtypes based on the various Raman technologies. First, the cells were evaluated structurally and spectrally. Following that, the cells' Raman data was efficiently examined utilizing a variety of machine learning methods, such as neural networks and multivariate models, which were all assessed simultaneously. The reports unequivocally demonstrate the effectiveness of AI assisted Raman spectroscopy for neoplasm cell classification and prediction, with an accuracy of correct predictions on a single spectrum. A molecular probe previously reported the ability of Raman spectroscopy to discriminate between tumor and healthy tissue. Present a quick, quantitative, probabilistic tumor assessment that incorporates real-time error analysis.},
        keywords = {Artificial Intelligence, Diagnosis, Machine Learning, Raman spectroscopy, Tumor},
        month = {December},
        }