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Identification of mammary lesions in thermographic images: feature selection study using genetic algorithms and particle swarm optimization

Identification of mammary lesions in thermographic images: feature selection study using genetic... Purpose The incidence of breast cancer increases every year. Early detection of the disease is critical since the sooner the disease is discovered the better are the treatments and the chances of cure. Mammography is now the gold standard for the diagnosis of breast cancer, but this screening tool has some limitations. Infrared thermography is being studied as a complementary tool due to its benefits. The combination of specialized professionals with methods of digital image analysis in breast thermography can contribute to improve diagnosis performance. From this, several research groups have been proposing methods to treat these data. Feature selection plays a fundamental role in this process, since it may optimize the machine learning process. Methods In this study, we propose a feature selection approach using genetic algorithms (GA) and particle swarm optimization (PSO) in thermographic images with breast lesions. The main goal of this approach is to optimize the identification and classification of breast lesions. We used several classifiers to assess the performance of the subsets with selected features. Support vector machines were more effective in these experiments. Results It was possible to reduce from 169 features with accuracy of 91.12% to 57 features with accuracy of 87.08% http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

Identification of mammary lesions in thermographic images: feature selection study using genetic algorithms and particle swarm optimization

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Publisher
Springer Journals
Copyright
Copyright © 2019 by Sociedade Brasileira de Engenharia Biomedica
Subject
Engineering; Biomedical Engineering and Bioengineering; Biomaterials; Biomedical Engineering/Biotechnology
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-019-00024-z
Publisher site
See Article on Publisher Site

Abstract

Purpose The incidence of breast cancer increases every year. Early detection of the disease is critical since the sooner the disease is discovered the better are the treatments and the chances of cure. Mammography is now the gold standard for the diagnosis of breast cancer, but this screening tool has some limitations. Infrared thermography is being studied as a complementary tool due to its benefits. The combination of specialized professionals with methods of digital image analysis in breast thermography can contribute to improve diagnosis performance. From this, several research groups have been proposing methods to treat these data. Feature selection plays a fundamental role in this process, since it may optimize the machine learning process. Methods In this study, we propose a feature selection approach using genetic algorithms (GA) and particle swarm optimization (PSO) in thermographic images with breast lesions. The main goal of this approach is to optimize the identification and classification of breast lesions. We used several classifiers to assess the performance of the subsets with selected features. Support vector machines were more effective in these experiments. Results It was possible to reduce from 169 features with accuracy of 91.12% to 57 features with accuracy of 87.08%

Journal

Research on Biomedical Engineering – Springer Journals

Published: Nov 20, 2019

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