Human glioma tumor diagnosis using visible resonance Raman spectroscopy and deep learning

Nathaniel E. Llanos; Yan Zhou; Xinguang Yu; Shengjia Zhang; Ke Zhu; Cheng-hui Liu; Binlin Wu; Robert R. Alfano

doi:10.1117/12.3009511

12 March 2024 Human glioma tumor diagnosis using visible resonance Raman spectroscopy and deep learning

Nathaniel E. Llanos, Yan Zhou, Xinguang Yu, Shengjia Zhang, Ke Zhu, Cheng-hui Liu, Binlin Wu, Robert R. Alfano

Proceedings Volume 12839, Biomedical Vibrational Spectroscopy 2024: Advances in Research and Industry; 1283908 (2024) https://doi.org/10.1117/12.3009511
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

Convolutional neural network (CNN) based deep learning is used to analyze spectral data collected by visible resonance Raman (VRR) spectroscopy to distinguish human glioma tumors from healthy brain tissues using binary classification and identify the cancer grades of the glioma tumors using multi-class classification. Classification was performed using both raw spectral data and baseline-subtracted data for comparison. The classification using both datasets yielded high accuracy, with the results obtained from baseline subtracted spectra slightly better than that obtained from raw spectra. The study showed VRR combined with deep learning provides a robust molecular diagnostic tool for accurately distinguishing glioma tumors from normal tissues and glioma tumor tissues at different cancer grades. Deep learning aided VRR technique may be used for in-situ intraoperative diagnosis of brain cancer. It may help a surgeon to identify cancer margins and even cancer grades during surgery.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nathaniel E. Llanos, Yan Zhou, Xinguang Yu, Shengjia Zhang, Ke Zhu, Cheng-hui Liu, Binlin Wu, and Robert R. Alfano "Human glioma tumor diagnosis using visible resonance Raman spectroscopy and deep learning", Proc. SPIE 12839, Biomedical Vibrational Spectroscopy 2024: Advances in Research and Industry, 1283908 (12 March 2024); https://doi.org/10.1117/12.3009511

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