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Machine Learning and Infrared Thermography for Breast Cancer Detection

 
: Gonçalves, Caroline Barcelos; Leles, Amanda Cristine Queiroz; Oliveira, Lucimara E.; Guimaraes, Gilmar; Cunha, Juliano Rodrigues; Fernandes, Henrique Coelho

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Volltext urn:nbn:de:0011-n-5617303 (203 KByte PDF)
MD5 Fingerprint: 03b017745165f804db3f7cafc1620f47
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Erstellt am: 16.10.2019


Proceedings. MDPI AG 27 (2019), Nr.1, Art. 45, 5 S.
ISSN: 2504-3900
International Workshop on Advanced Infrared Technology and Applications (AITA) <15, 2019, Firenze>
Englisch
Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation
Fraunhofer IZFP ()
breast cancer; infrared thermography; machine learning

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.

: http://publica.fraunhofer.de/dokumente/N-561730.html