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Area of Science:

  • Digital pathology
  • Machine learning in medical imaging
  • Computational pathology

Background:

  • Machine learning, particularly CNNs, has advanced medical imaging analysis.
  • Digital pathology and image analysis improve cervical cancer diagnostics.
  • Current commercial platforms are expensive and lack customization.

Purpose of the Study:

  • Introduce CINNAMON-GUI, an open-source digital pathology tool.
  • Enhance Pap smear image classification using CNNs.
  • Provide a customizable and scalable alternative to commercial platforms.

Main Methods:

  • Developed CINNAMON-GUI as a Python-based Shiny app.
  • Integrated advanced CNN models for digital pathology.
  • Implemented features for intuitive UI, real-time analysis, and customizable training.

Main Results:

  • Compared two CNN models (A and B), with Model B showing improved validation accuracy (0.95).
  • Feature mapping identified key morphological features for classification.
  • Model B significantly reduced misclassification errors compared to Model A.

Conclusions:

  • CINNAMON-GUI offers a transparent, open-source digital pathology solution.
  • The tool improves diagnostic accuracy via feature analysis and optimized CNNs.
  • Future work includes expanding applications to other cancer types.