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

Conference Paper

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 2020

Published In

  • Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual International Conference

Volume / Issue

  • 2020 /

Start / End Page

  • 1148 - 1151

PubMed ID

  • 33018190

Electronic International Standard Serial Number (EISSN)

  • 2694-0604

International Standard Serial Number (ISSN)

  • 2375-7477

Digital Object Identifier (DOI)

  • 10.1109/embc44109.2020.9175858