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Related Experiment Video

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An adaptive enhanced human memory algorithm for multi-level image segmentation for pathological lung cancer images.

Mahmoud Abdel-Salam1, Essam H Houssein2, Marwa M Emam2

  • 1Faculty of Computers and Information Science, Mansoura University, Mansoura, Egypt.

Computers in Biology and Medicine
|October 15, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces ASG-HMO, a novel algorithm for enhanced lung cancer image segmentation. ASG-HMO significantly improves diagnostic accuracy and speed compared to existing methods.

Keywords:
Human memory algorithmImage segmentationLung cancerMedical imagingPathological

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Area of Science:

  • Medical Imaging
  • Computational Intelligence
  • Pathology

Background:

  • Accurate lung cancer diagnosis relies on precise medical image segmentation.
  • Traditional metaheuristic methods face challenges in speed and accuracy for lung cancer image segmentation.

Purpose of the Study:

  • To introduce ASG-HMO, an enhanced Human Memory Optimization algorithm for improved multi-thresholding segmentation of lung cancer images.
  • To address limitations of existing methods in convergence speed and segmentation accuracy.

Main Methods:

  • ASG-HMO integrates enhanced adaptive mutualism, spiral motion, gaussian mutation, and adaptive t-distribution disturbance strategies.
  • The algorithm utilizes 2D Renyi entropy and 2D histograms for enhanced segmentation precision.
  • Validated on benchmark datasets (IEEE CEC'17, CEC'20) and applied to histopathology lung cancer images.

Main Results:

  • ASG-HMO demonstrated superior performance in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Feature Similarity Index Measure (FSIM), and Probability Rand Index (PRI).
  • Achieved a maximum PSNR of 31.924, SSIM of 0.919, FSIM of 0.990, and PRI of 0.924.
  • Outperformed existing algorithms in both convergence speed and segmentation accuracy.

Conclusions:

  • ASG-HMO offers a robust framework for precise pathological lung cancer image segmentation.
  • The enhanced algorithm has significant potential to improve clinical diagnostic processes.
  • ASG-HMO represents a substantial advancement in automated medical image analysis for oncology.