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2019
Journal Article
Titel
Machine Learning and Infrared Thermography for Breast Cancer Detection
Abstract
Breast cancer kills a large number of women around the world. Infrared thermography is a promising screening technique which does not involve harmful radiation for the patient and has a relatively low cost. This work proposes an approach for classifying patients into three different classes using infrared images: healthy patients, patients with benign changes and patients with cancer (malignant changes). A set of features is extracted from each image and two approaches are used in the classification process. The first is based on Artificial Neural Networks while the second is based on Support Vector Machines. The proposed approach shows a great potential to be used as a screening diagnosis technique for early breast cancer detection.
Author(s)
Gonçalves, Caroline Barcelos
School of Computer Science, Federal University of Uberlandia, Uberlandia, Brazil
Leles, Amanda Cristine Queiroz
School of Mechanical Engineering, Federal University of Uberlandia, Uberlandia, Brazil
Oliveira, Lucimara E.
School of Mechanical Engineering, Federal University of Uberlandia, Uberlandia, Brazil
Guimaraes, Gilmar
School of Mechanical Engineering, Federal University of Uberlandia, Uberlandia, Brazil
Cunha, Juliano Rodrigues
Cancer Hospital of Uberlandia, Federal University of Uberlandia, Uberlandia, Brazil