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Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Local-Ternary-Pattern-Based Associated Histogram Equalization Technique for Cervical Cancer Detection.

Saravanan Srinivasan1, Aravind Britto Karuppanan Raju2, Sandeep Kumar Mathivanan3

  • 1Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India.

Diagnostics (Basel, Switzerland)
|February 11, 2023
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Summary
This summary is machine-generated.

This study introduces an advanced method for early cervical cancer detection using cervigram images. The novel approach achieves high accuracy in identifying and segmenting cancerous regions, outperforming traditional techniques.

Keywords:
associated histogram equalization techniquecervigramenhanced local ternary patternfinite ridgelet transformgray-level run-length matricesmorphological operation

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Biomedical Engineering

Background:

  • Cervical cancer remains a significant global health concern for women.
  • Early detection and prompt treatment are crucial for improving patient outcomes and survival rates.

Purpose of the Study:

  • To develop and validate a novel strategy for enhanced cervical cancer detection using cervigram images.
  • To improve the accuracy and efficiency of identifying and segmenting cancerous regions in cervical images.

Main Methods:

  • Image preprocessing using Adaptive Histogram Equalization (AHE) and Finite Ridgelet Transform for multi-resolution analysis.
  • Feature extraction including ridgelets, gray-level run-length matrices, moment invariants, and enhanced local ternary patterns.
  • Classification using a feed-forward backpropagation neural network and segmentation via morphological operations.

Main Results:

  • The proposed system achieved high performance metrics: 98.11% sensitivity, 98.97% specificity, and 99.19% accuracy.
  • Positive Predictive Value (PPV) was 98.88%, Negative Predictive Value (NPV) was 91.91%, and precision reached 98.13%.
  • The method demonstrated superior performance in detecting and segmenting cervical cancer compared to traditional approaches.

Conclusions:

  • The developed system offers a promising and effective tool for early and accurate cervical cancer detection.
  • The integration of advanced image processing and machine learning techniques significantly enhances diagnostic capabilities.