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Multiple adaptive neuro-fuzzy inference system with automatic features extraction algorithm for cervical cancer

Mohammad Subhi Al-batah1, Nor Ashidi Mat Isa2, Mohammad Fadel Klaib3

  • 1Department of Computer Science and Software Engineering, Faculty of Science and Information Technology, Jadara University, P.O. Box 733, Irbid, Jordan.

Computational and Mathematical Methods in Medicine
|April 8, 2014
PubMed
Summary

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This summary is machine-generated.

This study introduces an intelligent computer vision system to improve cervical cancer screening. The system accurately classifies cell images, aiding pathologists and enhancing diagnostic accuracy for early detection.

Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Computational Pathology

Background:

  • Cervical cancer remains a significant global health concern for women.
  • Current screening methods like Pap smear and liquid-based cytology (LBC) are subjective and time-consuming.
  • Pathologist expertise heavily influences the accuracy of traditional cervical cancer screening.

Purpose of the Study:

  • To develop an intelligent computer vision system to assist pathologists in cervical cancer screening.
  • To overcome the subjectivity and time constraints associated with manual cytological analysis.
  • To improve the accuracy and efficiency of classifying cervical cell images.

Main Methods:

  • A two-stage intelligent system was developed for cervical cell image analysis.

Related Experiment Videos

  • Stage 1: Automatic Feature Extraction (AFE) algorithm for image analysis.
  • Stage 2: Multiple Adaptive Neuro-Fuzzy Inference System (MANFIS) for classification, utilizing parallel ANFIS models.

    • Main Results:

    • The AFE algorithm demonstrated effectiveness comparable to manual feature extraction by experts.
    • The MANFIS model achieved a high classification performance with 94.2% accuracy.
    • The system successfully classified cervical cell images into normal, low-grade squamous intraepithelial lesion (LSIL), and high-grade squamous intraepithelial lesion (HSIL) categories.

    Conclusions:

    • The developed computer vision system offers a promising tool to enhance cervical cancer diagnosis.
    • The intelligent system can assist pathologists, leading to more objective and accurate screening results.
    • This approach has the potential to improve early detection rates and patient outcomes for cervical cancer.