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Classification and Recognition of Ovarian Cells Based on Two-Dimensional Light Scattering Technology

  • Qi Chen
  • Jianling ZhangEmail author
Systems-Level Quality Improvement
  • 29 Downloads
Part of the following topical collections:
  1. Artificial Intelligence Application in Health Informatics

Abstract

Ovarian cancer is a very insidious malignant tumor. In order to detect ovarian cancer cells early, the classification and recognition of ovarian cancer cells is mainly studied by two-dimensional light scattering technology. Firstly, a single-cell two-dimensional light scattering pattern acquisition platform based on single-mode optical fiber illumination is designed to collect a certain number of two-dimensional light scattering patterns of ovarian cancer cells and normal ovarian cells. Then, the HOG (Histogram of Oriented Gradient) algorithm is used to extract shaving anisotropy feature of two-dimensional light scattering pattern. The results show that the accuracy of classification and identification of ovarian cancer cells by two-dimensional light scattering technology is 90.81%, which suggests that the specificity of cancer cells and normal cells can be characterized by two-dimensional light scattering technology.

Keywords

Ovarian cancer cell detection Two-dimensional light scattering patterns HOG feature extraction Machine learning 

Notes

Compliance with ethical standards

Conflict of interest

Author Qi Chen declares that he has no conflict of interest. Author Jianling Zhang declares that he has no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Gynaecology and ObstetricsZoucheng People’s HospitalZouchengChina

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