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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Efficient feature selection for histopathological image classification with improved multi-objective WOA.

Ravi Sharma¹, Kapil Sharma², Manju Bala³

¹Delhi Technological University, Bawana, New Delhi, 110042, India. ravisrma1988@gmail.com.

Scientific Reports

|October 25, 2024

Summary

This summary is machine-generated.

This study introduces an enhanced multi-objective whale optimization algorithm for efficient feature selection in histopathology image analysis, improving accuracy and reducing computation time compared to existing methods.

Keywords:

Image classification Multi-objective grey wolf optimizer Optimization algorithm Pre processing

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Area of Science:

Computational pathology
Bioinformatics
Machine learning

Background:

Efficient feature selection is critical for histopathology image analysis but remains a challenge.
Current methods often treat feature selection as a single-objective problem, limiting their effectiveness.

Purpose of the Study:

To propose an enhanced multi-objective whale optimization algorithm (EMOWOA) for feature selection in histopathology.
To address the limitations of single-objective approaches in complex image analysis tasks.

Main Methods:

Developed and utilized an enhanced multi-objective whale optimization algorithm (EMOWOA).
Validated EMOWOA on 10 standard multi-objective benchmark functions (CEC2009).
Compared EMOWOA against three existing feature selection techniques using five classifiers, evaluating accuracy, selected features, and computation time.

Main Results:

The proposed EMOWOA demonstrated superior optimization capabilities on benchmark functions.
EMOWOA significantly outperformed existing methods across evaluated parameters (accuracy, feature count, time).
The algorithm effectively mines optimal feature sets for histopathology image analysis.

Conclusions:

The enhanced multi-objective whale optimization algorithm offers a robust solution for efficient feature selection in histopathology.
This approach improves upon traditional single-objective methods, providing better performance metrics.
The findings highlight the potential of multi-objective optimization for advancing computational pathology.