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Feature Selection Based on Dialectical Optimization Algorithm for Breast Lesion Classification in Thermographic Images
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Author(s): Jessiane Mônica Silva Pereira (Universidade Federal de Pernambuco, Brazil), Maíra Araújo de Santana (Universidade Federal de Pernambuco, Brazil), Clarisse Lins de Lima (Universidade Federal de Pernambuco, Brazil), Rita de Cássia Fernandes de Lima (Universidade Federal de Pernambuco, Brazil), Sidney Marlon Lopes de Lima (Universidade Federal de Pernambuco, Brazil)and Wellington Pinheiro dos Santos (Universidade Federal de Pernambuco, Brazil)
Copyright: 2021
Pages: 25
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.ch004
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Abstract
Breast cancer is the leading cause of death among women worldwide. Early detection and early treatment are critical to minimize the effects of this disease. In this sense, breast thermography has been explored in the process of diagnosing this type of cancer. Furthermore, in an attempt to optimize the diagnosis, intelligent pattern recognition techniques are being used. Features selection performs an essential task in this process to optimize these intelligent techniques. This chapter proposes a features selection method using Dialectical Optimization Method (ODM) associated to a KNN classifier. The authors found that this combination proved to be a good approach showing a low impact on breast lesion classification performance. They obtained around 5% decrease in accuracy, with a reduction of about 46.80% of the features vector. The specificity and sensitivity values they found were competitive to other widely used methods.
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