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Classification of Breast Lesions in Frontal Thermographic Images Using a Diagnosis Aid Intelligent System
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Author(s): Maíra Araújo de Santana (Universidade Federal de Pernambuco, Brazil), Jessiane Mônica Silva Pereira (Universidade Federal de Pernambuco, Brazil), Clarisse Lins de Lima (Universidade Federal de Pernambuco, Brazil), Maria Beatriz Jacinto de Almeida (Universidade Federal de Pernambuco, Brazil), José Filipe Silva de Andrade (Universidade Federal de Pernambuco, Brazil), Thifany Ketuli Silva de Souza (Universidade Federal de Pernambuco, Brazil), Rita de Cássia Fernandes de Lima (Universidade Federal de Pernambuco, Brazil)and Wellington Pinheiro dos Santos (Universidade Federal de Pernambuco, Brazil)
Copyright: 2021
Pages: 19
Source title:
Biomedical Computing for Breast Cancer Detection and Diagnosis
Source Author(s)/Editor(s): Wellington Pinheiro dos Santos (Universidade Federal de Pernambuco, Brazil), Washington Wagner Azevedo da Silva (Universidade Federal de Pernambuco, Brazil)and Maira Araujo de Santana (Universidade Federal de Pernambuco, Brazil)
DOI: 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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