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

Amanda Rodrigues da Silva, Maíra Araújo de Santana, Clarisse Lins de Lima, José Silva de Andrade, Thifany Silva de Souza, Maria Jacinto de Almeida, Washington Azevedo da Silva, Rita Fernandes de Lima, Wellington Pinheiro dos Santos

OAI: oai:igi-global.com:277431 • DOI: 10.4018/IJAIML.20210701.oa1

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.

Computer Science and Information Technology Artificial Intelligence Machine Learning