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Computer-aided Diagnosis of Polyp Classification Using Scale Invariant Features and Extreme Gradient Boosting.

S Don1

  • 1Department of Computer Science and Applications, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India.

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Summary

This study developed a machine learning framework for classifying colorectal polyps from colonoscopy images. The proposed method achieved high accuracy, outperforming state-of-the-art techniques for polyp classification.

Keywords:
FractalRadon transformZernike movementgradient boosting algorithmpolypsspectral reflection

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Computational Pathology

Background:

  • Colorectal cancer diagnosis relies on accurate colonoscopy image analysis.
  • Machine learning models have shown promise in classifying colorectal polyps.
  • Advancements in technology enable sophisticated analysis of endoscopic imagery.

Purpose of the Study:

  • To develop a framework for classifying polyps from colonoscopy images.
  • To enhance the diagnostic accuracy of colorectal cancer identification.
  • To evaluate the performance of a novel polyp classification approach.

Main Methods:

  • Colonoscopy images were preprocessed to remove spectral reflections.
  • Scale-invariant features were extracted using Radon transform and Zernike moments.
  • Extreme Gradient Boosting (XGBoost) algorithm was employed for polyp classification.

Main Results:

  • The proposed framework achieved a classification accuracy of 93% with light XGBoost and 92% with XGBoost.
  • Cross-validation confirmed the robustness of the experimental results.
  • The method demonstrated superior performance compared to existing state-of-the-art techniques.

Conclusions:

  • XGBoost, combined with scale-invariant features, effectively classifies polyps from colonoscopy images.
  • The developed framework offers a promising tool for improving colorectal cancer diagnosis.
  • This approach shows potential for application in clinical settings with small datasets.