Combining multiple contrasts for improving machine learning-based classification of cervical cancers with a low-cost point-of-care Pocket colposcope

Published

Conference Paper

© 2020 IEEE. We apply feature-extraction and machine learning methods to multiple sources of contrast (acetic acid, Lugol's iodine and green light) from the white Pocket Colposcope, a low-cost point of care colposcope for cervical cancer screening. We combine features from the sources of contrast and analyze diagnostic improvements with addition of each contrast. We find that overall AUC increases with additional contrast agents compared to using only one source.

Full Text

Duke Authors

Cited Authors

  • Asiedu, MN; Skerrett, E; Sapiro, G; Ramanujam, N

Published Date

  • July 1, 2020

Published In

Volume / Issue

  • 2020-July /

Start / End Page

  • 1148 - 1151

International Standard Serial Number (ISSN)

  • 1557-170X

International Standard Book Number 13 (ISBN-13)

  • 9781728119908

Digital Object Identifier (DOI)

  • 10.1109/EMBC44109.2020.9175858

Citation Source

  • Scopus