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A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification.

Teresa Conceição1, Cristiana Braga2, Luís Rosado3

  • 1Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal. teresa.psconc@gmail.com.

International Journal of Molecular Sciences
|October 18, 2019
PubMed
Summary
This summary is machine-generated.

Computer-aided diagnosis shows promise for improving cervical cancer screening, addressing workflow issues and aiding low-income countries. This review analyzes computational methods for automated cervical cell analysis to support new diagnostic tools.

Keywords:
cervical cancerclassificationcomputer-aided diagnosismachine learningpap-smearscreeningsegmentation

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Area of Science:

  • Oncology
  • Medical Imaging
  • Computer Science

Background:

  • Cervical cancer is a leading global cancer in women, with current screening methods facing challenges like lengthy workflows and ambiguity.
  • These challenges are particularly pronounced in low-income regions, contributing to higher mortality rates.
  • There is a growing need for efficient and accurate cervical cancer screening solutions.

Purpose of the Study:

  • To provide an overview of cervical cancer and its screening procedures.
  • To analyze computational methods for automated cervical cell analysis, focusing on quality assessment, segmentation, and classification.
  • To support the development of next-generation computer-aided diagnosis (CADx) systems for cervical screening.

Main Methods:

  • Extensive literature review of computational methods for cervical cell analysis.
  • Focus on automated quality assessment, segmentation, and classification techniques.
  • Critical discussion of existing methods and their relevance to practical application.

Main Results:

  • Identification and analysis of key computational approaches for cervical cell analysis.
  • Evaluation of methods for automated quality assessment, segmentation, and classification.
  • Discussion of the strengths and limitations of current computer-aided methods.

Conclusions:

  • Computer-aided diagnosis systems offer significant potential to enhance cervical cancer screening efficiency and accuracy.
  • Further research into automated tools is crucial for overcoming current screening limitations, especially in resource-limited settings.
  • The review highlights future research directions for developing advanced CADx systems in cervical cancer screening.