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Two‐Dimensional Light Scattering Anisotropy Cytometry for Label‐Free Classification of Ovarian Cancer Cells via Machine Learning
Author(s) -
Su Xuantao,
Yuan Tao,
Wang Zhiwen,
Song Kun,
Li Rongrong,
Yuan Cunzhong,
Kong Beihua
Publication year - 2020
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.23865
Subject(s) - anisotropy , cytometry , light scattering , flow cytometry , histogram , artificial intelligence , support vector machine , scattering , cancer cell , computer science , ovarian cancer , pattern recognition (psychology) , cancer , physics , biology , optics , medicine , microbiology and biotechnology , image (mathematics)
We develop a single‐mode fiber‐based cytometer for the obtaining of two‐dimensional (2D) light scattering patterns from static single cells. Anisotropy of the 2D light scattering patterns of single cells from ovarian cancer and normal cell lines is investigated by histograms of oriented gradients (HOG) method. By analyzing the HOG descriptors with support vector machine, an accuracy rate of 92.84% is achieved for the automatic classification of these two kinds of label‐free cells. The 2D light scattering anisotropy cytometry combined with machine learning may provide a label‐free, automatic method for screening of ovarian cancer cells, and other types of cells. © 2019 International Society for Advancement of Cytometry