Deep learning classification of cervical dysplasia using depth-resolved angular light scattering profiles.

Journal Article (Journal Article)

We present a machine learning method for detecting and staging cervical dysplastic tissue using light scattering data based on a convolutional neural network (CNN) architecture. Depth-resolved angular scattering measurements from two clinical trials were used to generate independent training and validation sets as input of our model. We report 90.3% sensitivity, 85.7% specificity, and 87.5% accuracy in classifying cervical dysplasia, showing the uniformity of classification of a/LCI scans across different instruments. Further, our deep learning approach significantly improved processing speeds over the traditional Mie theory inverse light scattering analysis (ILSA) method, with a hundredfold reduction in processing time, offering a promising approach for a/LCI in the clinic for assessing cervical dysplasia.

Full Text

Duke Authors

Cited Authors

  • Zhang, H; Kendall, WY; Jelly, ET; Wax, A

Published Date

  • August 2021

Published In

Volume / Issue

  • 12 / 8

Start / End Page

  • 4997 - 5007

PubMed ID

  • 34513238

Pubmed Central ID

  • PMC8407824

Electronic International Standard Serial Number (EISSN)

  • 2156-7085

International Standard Serial Number (ISSN)

  • 2156-7085

Digital Object Identifier (DOI)

  • 10.1364/boe.430467


  • eng