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Features Selection Study for Breast Cancer Diagnosis Using Thermographic Images, Genetic Algorithms, and Particle Swarm Optimization

Features Selection Study for Breast Cancer Diagnosis Using Thermographic Images, Genetic Algorithms, and Particle Swarm Optimization

Amanda Lays Rodrigues da Silva, Maíra Araújo de Santana, Clarisse Lins de Lima, José Filipe Silva de Andrade, Thifany Ketuli Silva de Souza, Maria Beatriz Jacinto de Almeida, Washington Wagner Azevedo da Silva, Rita de Cássia Fernandes de Lima, Wellington Pinheiro dos Santos
Copyright: © 2023 |Pages: 21
ISBN13: 9781668471364|ISBN10: 1668471361|EISBN13: 9781668471371
DOI: 10.4018/978-1-6684-7136-4.ch043
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MLA

Rodrigues da Silva, Amanda Lays, et al. "Features Selection Study for Breast Cancer Diagnosis Using Thermographic Images, Genetic Algorithms, and Particle Swarm Optimization." Research Anthology on Medical Informatics in Breast and Cervical Cancer, edited by Information Resources Management Association, IGI Global, 2023, pp. 823-843. https://doi.org/10.4018/978-1-6684-7136-4.ch043

APA

Rodrigues da Silva, A. L., Araújo de Santana, M., Lins de Lima, C., Silva de Andrade, J. F., Silva de Souza, T. K., Jacinto de Almeida, M. B., Azevedo da Silva, W. W., Fernandes de Lima, R. D., & Pinheiro dos Santos, W. (2023). Features Selection Study for Breast Cancer Diagnosis Using Thermographic Images, Genetic Algorithms, and Particle Swarm Optimization. In I. Management Association (Ed.), Research Anthology on Medical Informatics in Breast and Cervical Cancer (pp. 823-843). IGI Global. https://doi.org/10.4018/978-1-6684-7136-4.ch043

Chicago

Rodrigues da Silva, Amanda Lays, et al. "Features Selection Study for Breast Cancer Diagnosis Using Thermographic Images, Genetic Algorithms, and Particle Swarm Optimization." In Research Anthology on Medical Informatics in Breast and Cervical Cancer, edited by Information Resources Management Association, 823-843. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-7136-4.ch043

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Abstract

Early detection of breast cancer is critical to improve treatment efficiency and chance of cure. Mammography is the main method for breast cancer screening; however, it has some limitations. Infrared thermography is a technique that is being studied for its benefits. The existing tumor classification systems are detailed, complex, and have low usability. Therefore, combining specialized professionals with methods of digital image analysis using thermography can help improve the diagnosis. Considering this, some computational areas are working on studies and creating methods to assess these data. The features selection plays a key role in this process, as it is a way to help solving data multidimensionality problems. This study aims to reduce the amount of features from thermographic images with mammary lesions. The authors used genetic algorithm and particle swarm optimization for features selection and compared the performance of each method to the performance using the entire set of features.