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Related Experiment Video

Updated: May 3, 2026

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Automatic cervical cell segmentation and classification in Pap smears.

Thanatip Chankong1, Nipon Theera-Umpon2, Sansanee Auephanwiriyakul3

  • 1Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand.

Computer Methods and Programs in Biomedicine
|January 18, 2014
PubMed
Summary

This study introduces an automated method for segmenting and classifying cervical cancer cells using fuzzy C-means clustering and artificial neural networks. The approach achieved high accuracy in detecting various stages of cervical abnormalities, outperforming other techniques.

Keywords:
Cervical cancer screeningCervical cell classificationCervical cell segmentationPap smearThinPrep

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Area of Science:

  • Medical imaging analysis
  • Computational pathology
  • Oncology

Background:

  • Cervical cancer is a leading cause of cancer death in women globally.
  • Early diagnosis through screening methods like the Papanicolaou (Pap) test is crucial for curable outcomes.
  • Automated analysis of cell images can aid in early and accurate detection.

Purpose of the Study:

  • To develop and evaluate an automated method for segmenting and classifying cervical cancer cells.
  • To compare the performance of the proposed method against existing techniques.
  • To assess the accuracy of artificial neural networks in classifying different grades of cervical lesions.

Main Methods:

  • Cell images were segmented into nucleus, cytoplasm, and background using fuzzy C-means (FCM) clustering.
  • Four datasets (ERUDIT, LCH, Herlev) with varying cell classes were used, including normal, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), and squamous cell carcinoma (SCC).
  • Artificial neural networks (ANN) were employed for classification, alongside Bayesian classifier, LDA, KNN, and SVM, with nucleus-based and cell-based features.

Main Results:

  • ANN achieved high accuracies: 96.20% (4-class) and 97.83% (2-class) on ERUDIT dataset.
  • ANN achieved high accuracies: 93.78% (7-class) and 99.27% (2-class) on Herlev dataset.
  • ANN achieved high accuracies: 95.00% (4-class) and 97.00% (2-class) on LCH dataset.
  • The proposed automatic approach demonstrated superior performance compared to hard C-means clustering and watershed techniques.

Conclusions:

  • The developed automated segmentation and classification method shows very good performance for cervical cancer detection.
  • Artificial neural networks, particularly with nucleus-based and cell-based features, are effective classifiers for cervical cell images.
  • The proposed FCM-based approach offers an improved alternative to traditional segmentation and classification methods for cervical cancer screening.