Paper
4 March 2019 Breast cancer diagnosis using fluorescence spectroscopy with dual-wavelength excitation and machine learning
Xin Gao, Binlin Wu
Author Affiliations +
Abstract
Intrinsic fluorescence spectra of fresh normal and cancerous human breast tissues were measured using two selective excitation wavelengths including 290nm and 340nm. Dual-wavelength excitation may reveal more molecular information than single-wavelength excitation. In the meantime, it is significantly faster than the acquisition of excitation-emission (EEM) matrix. Unsupervised machine learning algorithms principal component analysis (PCA) and non-negative matrix factorization (NMF) were used to reduce the dimensionality of the spectral data. The relative concentrations of the basis spectra retrieved by PCA and NMF were considered features of the samples and used to distinguish normal and malignant tissues. The performances of classification using support vector machine (SVM) based on PCA and NMF features were compared. The classification using spectral data with dual-wavelength excitation was compared with single-wavelength excitation. Classification based on NMF-retrieved components from spectral data with dual-wavelength excitation yielded the best performance.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xin Gao and Binlin Wu "Breast cancer diagnosis using fluorescence spectroscopy with dual-wavelength excitation and machine learning", Proc. SPIE 10873, Optical Biopsy XVII: Toward Real-Time Spectroscopic Imaging and Diagnosis, 108731F (4 March 2019); https://doi.org/10.1117/12.2509147
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KEYWORDS
Tissues

Principal component analysis

Biopsy

Breast cancer

Luminescence

Fluorescence spectroscopy

Breast

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