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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: © 2023 |Pages: 20
ISBN13: 9781668471364|ISBN10: 1668471361|EISBN13: 9781668471371
DOI: 10.4018/978-1-6684-7136-4.ch004
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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." Research Anthology on Medical Informatics in Breast and Cervical Cancer, edited by Information Resources Management Association, IGI Global, 2023, pp. 76-95. https://doi.org/10.4018/978-1-6684-7136-4.ch004

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. (2023). Classification of Breast Lesions in Frontal Thermographic Images Using a Diagnosis Aid Intelligent System. In I. Management Association (Ed.), Research Anthology on Medical Informatics in Breast and Cervical Cancer (pp. 76-95). IGI Global. https://doi.org/10.4018/978-1-6684-7136-4.ch004

Chicago

Araújo de Santana, Maíra, et al. "Classification of Breast Lesions in Frontal Thermographic Images Using a Diagnosis Aid Intelligent System." In Research Anthology on Medical Informatics in Breast and Cervical Cancer, edited by Information Resources Management Association, 76-95. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-7136-4.ch004

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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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