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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.

Applied Bionics and Biomechanics

|April 1, 2022

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.