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

Publication ,  Journal Article
Asiedu, MN; Simhal, A; Chaudhary, U; Mueller, JL; Lam, CT; Schmitt, JW; Venegas, G; Sapiro, G; Ramanujam, N
Published in: IEEE Trans Biomed Eng
August 2019

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.

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Published In

IEEE Trans Biomed Eng

DOI

EISSN

1558-2531

Publication Date

August 2019

Volume

66

Issue

8

Start / End Page

2306 / 2318

Location

United States

Related Subject Headings

  • Uterine Cervical Neoplasms
  • Precancerous Conditions
  • Point-of-Care Systems
  • Machine Learning
  • Image Interpretation, Computer-Assisted
  • Humans
  • Female
  • Early Detection of Cancer
  • Colposcopes
  • Cervix Uteri
 

Citation

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Asiedu, M. N., Simhal, A., Chaudhary, U., Mueller, J. L., Lam, C. T., Schmitt, J. W., … Ramanujam, N. (2019). Development of Algorithms for Automated Detection of Cervical Pre-Cancers With a Low-Cost, Point-of-Care, Pocket Colposcope. IEEE Trans Biomed Eng, 66(8), 2306–2318. https://doi.org/10.1109/TBME.2018.2887208
Asiedu, Mercy Nyamewaa, Anish Simhal, Usamah Chaudhary, Jenna L. Mueller, Christopher T. Lam, John W. Schmitt, Gino Venegas, Guillermo Sapiro, and Nimmi Ramanujam. “Development of Algorithms for Automated Detection of Cervical Pre-Cancers With a Low-Cost, Point-of-Care, Pocket Colposcope.IEEE Trans Biomed Eng 66, no. 8 (August 2019): 2306–18. https://doi.org/10.1109/TBME.2018.2887208.
Asiedu MN, Simhal A, Chaudhary U, Mueller JL, Lam CT, Schmitt JW, et al. 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):2306–18.
Asiedu, Mercy Nyamewaa, et al. “Development of Algorithms for Automated Detection of Cervical Pre-Cancers With a Low-Cost, Point-of-Care, Pocket Colposcope.IEEE Trans Biomed Eng, vol. 66, no. 8, Aug. 2019, pp. 2306–18. Pubmed, doi:10.1109/TBME.2018.2887208.
Asiedu MN, Simhal A, Chaudhary U, Mueller JL, Lam CT, Schmitt JW, Venegas G, Sapiro G, Ramanujam N. 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.

Published In

IEEE Trans Biomed Eng

DOI

EISSN

1558-2531

Publication Date

August 2019

Volume

66

Issue

8

Start / End Page

2306 / 2318

Location

United States

Related Subject Headings

  • Uterine Cervical Neoplasms
  • Precancerous Conditions
  • Point-of-Care Systems
  • Machine Learning
  • Image Interpretation, Computer-Assisted
  • Humans
  • Female
  • Early Detection of Cancer
  • Colposcopes
  • Cervix Uteri