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A Fast Hybrid Classification Algorithm with Feature Reduction for Medical Images.

Hanan Ahmed Hosni Mahmoud1, Abeer Abdulaziz AlArfaj1, Alaaeldin M Hafez2

  • 1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

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|April 1, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a fast hybrid fuzzy classification algorithm for medical image analysis. The novel approach enhances cervical cancer detection using quantum-based grasshopper computing and fuzzy C-means for feature reduction.

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Computational Intelligence

Background:

  • Cervical cancer diagnosis relies heavily on analyzing cellular features from Pap smear images.
  • Accurate feature extraction and reduction are critical for improving classification precision in medical image processing.

Purpose of the Study:

  • To develop a fast hybrid fuzzy classification algorithm integrated with feature reduction for enhanced medical image analysis.
  • To apply this technique specifically for cervical cancer detection using Pap smear images.

Main Methods:

  • Incorporation of a quantum-based grasshopper computing algorithm (QGH) with fuzzy C-means clustering for feature extraction.
  • Utilizing a dataset of over 2600 public Pap smear images for feature extraction and reduction.
  • Performance evaluation through two experimental setups: one with all features and another with QGH-fuzzy C-means for optimal feature selection.

Main Results:

  • The proposed technique effectively extracts and reduces features from Pap smear images, crucial for cancer diagnosis.
  • Experimental setups demonstrated the influence of selected features on classification accuracy across different cancer categories.
  • A fusion technique combining QGH, fuzzy C-means, and statistical methods showed qualitative agreement in feature selection.

Conclusions:

  • The developed hybrid fuzzy classification algorithm offers a fast and effective method for medical image analysis.
  • Feature reduction using QGH and fuzzy C-means significantly impacts classification precision in cervical cancer detection.
  • The integration of quantum computing principles enhances machine learning applications in medical diagnostics.