sensors Article Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography Roger Resmini 1,2 , Lincoln Silva 2,3 , Adriel S. Araujo 2,∗ , Petrucio Medeiros 4 , Débora Muchaluat-Saade 4 and Aura Conci 2 1 Institute of Exact and Natural Sciences, Federal University of Rondonópolis, Cidade Universitária, Rondonópolis 78736-900, MT, Brazil;
[email protected] 2 Visual Lab, Institute of Computing, Fluminense Federal University, Av. Gal. Milton Tavares de Souza, S/N, Niterói 24210-346, RJ, Brazil;
[email protected] (L.S.);
[email protected] (A.C.) 3 Advanced Research Medical Laboratory, Departament of Information Technology and Education in Health, Faculty of Medical Sciences, State University of Rio de Janeiro, R. Professor Manuel de Abreu, 444, Rio de Janeiro 20550-170, RJ, Brazil;
[email protected] (P.M.);
[email protected] (D.M.-S.) 4 Mídiacom Lab, Institute of Computing, Fluminense Federal University, R. Passo da Pátria 156, Niterói 24210-240, RJ, Brazil * Correspondence:
[email protected] Abstract: Breast cancer is one of the leading causes of mortality globally, but early diagnosis and treatment can increase the cancer survival rate. In this context, thermography is a suitable approach to help early diagnosis due to the temperature difference between cancerous tissues and healthy neighboring tissues. This work proposes an ensemble method for selecting models and features by combining a Genetic Algorithm (GA) and the Support Vector Machine (SVM) classifier to diagnose breast cancer. Our evaluation demonstrates that the approach presents a significant contribution Citation: Resmini, R.; Silva, L.; to the early diagnosis of breast cancer, presenting results with 94.79% Area Under the Receiver Araujo, A.S.; Medeiros, P.; Operating Characteristic Curve and 97.18% of Accuracy.