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Dynamic comprehensive learning-based dung beetle optimizer using triangular mutation for polyps image segmentation.

Mohamed Abd Elaziz1, Diego Oliva2, Alaa A El-Bary3

  • 1Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt; Faculty of Computer Science and Engineering, Galala University, Suze 435611, Egypt; Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates.

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Summary
This summary is machine-generated.

Early diagnosis of colon polyps is crucial for preventing colorectal cancer. A new multilevel thresholding technique using an enhanced Dung beetle optimizer significantly improves polyp image segmentation accuracy.

Keywords:
Dung beetle optimizer (DBO)Image segmentationMultilevel thresholdingPolyps images

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Early diagnosis of colon polyps is vital for colorectal cancer prevention and improved patient outcomes.
  • Current polyp detection methods require enhancement for greater accuracy and effectiveness.
  • Colorectal cancer often originates from polyps that can become cancerous over time.

Purpose of the Study:

  • To introduce an advanced multilevel thresholding technique for accurate colon polyp image segmentation.
  • To enhance the Dung beetle optimizer (DBO) algorithm for improved segmentation performance.
  • To evaluate the efficacy of the proposed DCTDBO method in segmenting polyp images.

Main Methods:

  • Development of a novel multilevel thresholding technique (MLTs) for polyp image segmentation.
  • Enhancement of the Dung beetle optimizer (DBO) using Triangular Mutation (TMO) and Comprehensive Learning (CL) operators, termed DCTDBO.
  • Performance evaluation of DCTDBO against other segmentation methods using eight polyp images.

Main Results:

  • The DCTDBO method demonstrated superior performance in segmenting colon polyp images.
  • DCTDBO achieved higher scores in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity (FSIM) compared to the basic DBO and other methods.
  • Average FSIM, SSIM, and PSNR values for DCTDBO were 0.9668, 0.99217, and 28.9338, respectively, indicating significant performance enhancement.

Conclusions:

  • The enhanced DBO algorithm, incorporating TMO and CL, effectively improves colon polyp image segmentation.
  • The proposed DCTDBO method offers a promising approach for early and accurate polyp diagnosis in preventive healthcare.
  • This advanced segmentation technique contributes to better colorectal cancer prognosis and treatment outcomes.