Development of Algorithms for Automated Detection of Cervical Pre-Cancers With a Low-Cost, Point-of-Care, Pocket Colposcope

IEEE Trans Biomed Eng. 2019 Aug;66(8):2306-2318. doi: 10.1109/TBME.2018.2887208. Epub 2018 Dec 18.


Goal: In this paper, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugol's iodine cervigrams and (2) methods for combining features/diagnosis of different contrasts in cervigrams for improved performance.

Methods: We developed algorithms to pre-process pathology-labeled cervigrams and extract simple but powerful color and textural-based features. The features were used to train a support vector machine model to classify cervigrams based on corresponding pathology for visual inspection with acetic acid, visual inspection with Lugol's iodine, and a combination of the two contrasts.

Results: The proposed framework achieved a sensitivity, specificity, and accuracy of 81.3%, 78.6%, and 80.0%, respectively, when used to distinguish cervical intraepithelial neoplasia (CIN+) relative to normal and benign tissues. This is superior to the average values achieved by three expert physicians on the same data set for discriminating normal/benign cases from CIN+ (77% sensitivity, 51% specificity, and 63% accuracy).

Conclusion: The results suggest that utilizing simple color- and textural-based features from visual inspection with acetic acid and visual inspection with Lugol's iodine images may provide unbiased automation of cervigrams.

Significance: This would enable automated, expert-level diagnosis of cervical pre-cancer at the point of care.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms*
  • Cervix Uteri / diagnostic imaging
  • Colposcopes*
  • Early Detection of Cancer / instrumentation
  • Early Detection of Cancer / methods
  • Female
  • Humans
  • Image Interpretation, Computer-Assisted / instrumentation
  • Image Interpretation, Computer-Assisted / methods*
  • Machine Learning
  • Point-of-Care Systems
  • Precancerous Conditions / diagnostic imaging*
  • Uterine Cervical Neoplasms / diagnostic imaging*