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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

High-performance breast cancer diagnosis method using hybrid feature selection method.

Mohammad Moradi¹, Abdalhossein Rezai²

¹ACECR Institute of Higher Education, Isfahan Branch, Isfahan, Iran.

Biomedizinische Technik. Biomedical Engineering

|December 22, 2024

Summary

This summary is machine-generated.

This study enhances breast cancer diagnosis using thermal imaging and a novel Computer Aided Diagnosis (CAD) system. The improved CAD system achieves high accuracy, aiding in early detection and treatment of breast cancer.

Keywords:

Computer Aided Diagnosis (CAD) system breast cancer feature extraction feature selection thermography

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Area of Science:

Biomedical Engineering
Medical Imaging
Artificial Intelligence in Medicine

Background:

Breast cancer is a leading cause of death in women, making early and accurate diagnosis critical.
Computer Aided Diagnosis (CAD) systems offer valuable support to clinicians in the diagnostic process.
Thermal imaging presents a non-invasive modality for breast cancer detection.

Purpose of the Study:

To enhance the performance of a breast cancer diagnosis CAD system.
To develop an efficient method for improving breast cancer diagnosis using thermal images.

Main Methods:

Employed the Segmentation Fractal Texture Analysis (SFTA) algorithm for feature extraction.
Utilized a hybrid feature selection algorithm combining binary grey wolf optimization and firefly optimization.
Applied kNN, SVM, and DTree classification techniques for performance evaluation.

Main Results:

The proposed method achieved high diagnostic performance metrics.
Accuracy: 97%, Specificity: 96%, Sensitivity: 98%, Matthews Correlation Coefficient (MCC): 94.17%.

Conclusions:

The developed CAD system demonstrates superior performance compared to existing methods for breast cancer diagnosis using thermal images.
The proposed approach offers an efficient and accurate tool for breast cancer detection.