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Feature Reduction in Graph Analysis.

Rapepun Piriyakul¹, Punpiti Piamsa-Nga²

¹Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Jatujak, Bangkok, 10900, Thailand. rapepunnight@yahoo.com.

Sensors (Basel, Switzerland)

|November 23, 2016

Summary

This study introduces a novel feature selection algorithm for medical image classification. The new method efficiently identifies essential features, reducing computational load without compromising cancer detection accuracy.

Keywords:

Feature Selection Graph Analysis Mammogram.Path Analysis

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Area of Science:

Medical Image Analysis
Machine Learning in Healthcare
Computational Biology

Background:

Improving medical image classification often involves adding features, increasing computational costs and not always enhancing performance.
Existing feature selection algorithms struggle to effectively remove redundant features in medical image analysis.
Over 50 features are commonly used in image feature classification literature.

Purpose of the Study:

To develop a new feature selection algorithm for medical image classification that reduces computational complexity.
To eliminate redundant features that offer similar contributions to classifier decisions.
To maintain or improve cancer detection accuracy with a reduced feature set.

Main Methods:

A novel feature selection algorithm based on graph analysis of feature interactions and feature-to-classifier decision relationships.
Modification of path analysis incorporating regression analysis, multiple logistic, and posterior Bayesian inference.
Experiments conducted on a database of 113 mammograms from the Mammographic Image Analysis Society.

Main Results:

A 13-feature set selected by the new algorithm achieved comparable cancer detection accuracy (true positive and false-positive rates) to a 26-feature set selected by Sequential Floating Selection (SFS) and using all 50 features.
The proposed algorithm significantly reduced the computational resources required for preprocessing and classifier training.
Tested classifiers included Artificial Neural Network (ANN) and logistic regression.

Conclusions:

The developed graph-based feature selection algorithm effectively identifies a minimal set of discriminative features for medical image classification.
This approach offers a computationally efficient alternative to existing methods, reducing processing time and resource demands.
The findings suggest that a reduced feature set can yield robust cancer detection performance in mammography.