Adjustable adaboost classifier and pyramid features for image-based cervical cancer diagnosis

Tao Xu, Edward Kim, Sharon Xiaolei Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Cervical cancer is the third most common type of cancer in women worldwide. Most death cases of cervical cancer occur in less developed areas of the world. In this work, we develop an automated and low-cost method that is applicable in those low-resource regions. First, we propose a more distinctive multi-feature descriptor for encoding the cervical image information by enhancing an existing descriptor with the pyramid histogram of local binary pattern (PLBP) feature. Second, we apply the AdaBoost algorithm to perform feature selection, and train a binary classifier to differentiate high-risk patient visits from low-risk patient visits. Our AdaBoost classifier can be adjusted to achieve high specificity, which is necessary for use in clinical practice. Experiments on both balanced and imbalanced datasets are conducted to evaluate the effectiveness of our method. Our method is shown to achieve better performance than existing image-based CIN classification systems and also outperform human interpretations on various screening tests.

Original languageEnglish (US)
Title of host publication2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PublisherIEEE Computer Society
Pages281-285
Number of pages5
ISBN (Electronic)9781479923748
DOIs
StatePublished - Jul 21 2015
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: Apr 16 2015Apr 19 2015

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2015-July
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
CountryUnited States
CityBrooklyn
Period4/16/154/19/15

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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    Xu, T., Kim, E., & Huang, S. X. (2015). Adjustable adaboost classifier and pyramid features for image-based cervical cancer diagnosis. In 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015 (pp. 281-285). [7163868] (Proceedings - International Symposium on Biomedical Imaging; Vol. 2015-July). IEEE Computer Society. https://doi.org/10.1109/ISBI.2015.7163868