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Simplified Convolutional Neural Network Application for Cervix Type Classification via Colposcopic Images.

Vitalii Pavlov1,2, Stanislav Fyodorov1, Sergey Zavjalov1

  • 1Higher School of Applied Physics and Space Technologies, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia.

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|June 23, 2022
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Summary

This study introduces a neural network for early cervical cancer detection, improving accuracy and reducing subjectivity in colposcopy. The AI classifier offers high recognition rates and low computational needs for accessible implementation.

Keywords:
cervical cancercolposcopyconvolutional neural networkspathologiessuspicious for invasion

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Area of Science:

  • Gynecologic Oncology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Colposcopy is crucial for early cervical cancer (CC) detection, a leading cancer in women globally, particularly in low-resource settings.
  • Current colposcopy methods, while effective, suffer from subjectivity and operator dependency, highlighting a need for improved diagnostic tools.
  • Advancements in artificial intelligence offer potential solutions to enhance the accuracy and objectivity of lesion detection in gynecologic procedures.

Purpose of the Study:

  • To develop and evaluate a novel neural network-based classifier for objective and accurate identification of cervical lesions.
  • To overcome the limitations of traditional colposcopy, such as subjectivity and reliance on operator expertise.
  • To create a computationally efficient AI model suitable for implementation on low-cost, low-power platforms.

Main Methods:

  • A neural network classifier was designed and trained to recognize and categorize cervical lesions from endoscopic images.
  • The model architecture incorporates a global averaging pooling level for enhanced simplicity and efficiency.
  • Performance was evaluated based on classification accuracy for distinct lesion categories: normal, LSIL, HSIL, and suspicious for invasion.

Main Results:

  • The neural network achieved high classification accuracies: 95.46% for normal, 79.78% for LSIL, 94.16% for HSIL, and 97.09% for suspicious for invasion.
  • The proposed architecture demonstrates lower computational complexity compared to existing methods.
  • The AI classifier exhibits a strong potential for accurate and objective cervical lesion detection.

Conclusions:

  • The developed neural network provides a promising, objective alternative to subjective colposcopy for cervical cancer screening.
  • Its high accuracy and computational efficiency make it suitable for cost-effective deployment, even on basic hardware.
  • This AI approach can significantly aid in the early detection of precancerous cervical lesions, improving patient outcomes worldwide.