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Classification of Breast Lesions in Frontal Thermographic Images Using a Diagnosis Aid Intelligent System

Classification of Breast Lesions in Frontal Thermographic Images Using a Diagnosis Aid Intelligent System

Maíra Araújo de Santana, Jessiane Mônica Silva Pereira, Clarisse Lins de Lima, Maria Beatriz Jacinto de Almeida, José Filipe Silva de Andrade, Thifany Ketuli Silva de Souza, Rita de Cássia Fernandes de Lima, Wellington Pinheiro dos Santos
Copyright: © 2021 |Pages: 19
ISBN13: 9781799834564|ISBN10: 1799834565|EISBN13: 9781799834571
DOI: 10.4018/978-1-7998-3456-4.ch003
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MLA

Araújo de Santana, Maíra, et al. "Classification of Breast Lesions in Frontal Thermographic Images Using a Diagnosis Aid Intelligent System." Biomedical Computing for Breast Cancer Detection and Diagnosis, edited by Wellington Pinheiro dos Santos, et al., IGI Global, 2021, pp. 28-46. https://doi.org/10.4018/978-1-7998-3456-4.ch003

APA

Araújo de Santana, M., Pereira, J. M., Lins de Lima, C., Jacinto de Almeida, M. B., Silva de Andrade, J. F., Silva de Souza, T. K., Fernandes de Lima, R. D., & Pinheiro dos Santos, W. (2021). Classification of Breast Lesions in Frontal Thermographic Images Using a Diagnosis Aid Intelligent System. In W. Pinheiro dos Santos, W. Azevedo da Silva, & M. de Santana (Eds.), Biomedical Computing for Breast Cancer Detection and Diagnosis (pp. 28-46). IGI Global. https://doi.org/10.4018/978-1-7998-3456-4.ch003

Chicago

Araújo de Santana, Maíra, et al. "Classification of Breast Lesions in Frontal Thermographic Images Using a Diagnosis Aid Intelligent System." In Biomedical Computing for Breast Cancer Detection and Diagnosis, edited by Wellington Pinheiro dos Santos, Washington Wagner Azevedo da Silva, and Maira Araujo de Santana, 28-46. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-3456-4.ch003

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Abstract

This study aims to assess the breast lesions classification in thermographic images using different configuration of an Extreme Learning Machine network as classifier. In this approach, the authors changed the number of neurons in the hidden layer and the type of kernel function to further explore the network in order to find a better solution for the classification problem. Authors also used different tools to perform features extraction to assess both texture and geometry information from the breast lesions. During the study, the authors found that the results changed not only due to the network parameters but also due to the features chosen to represent the thermographic images. A maximum accuracy of 95% was found for the differentiation of breast lesions.

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