Application of SVM classifier in thermographic image classification for early detection of breast cancer

Witold Oleszkiewicz , Paweł Cichosz , Dariusz Jagodziński , Mateusz Matysiewicz , Łukasz Neumann , Robert Marek Nowak , Rafał Okuniewski

Abstract

This article presents the application of machine learning algorithms for early detection of breast cancer on the basis of thermographic images. Supervised learning model: Support vector machine (SVM) and Sequential Minimal Optimization algorithm (SMO) for the training of SVM classifier were implemented. The SVM classifier was included in a client-server application which enables to create a training set of examinations and to apply classifiers (including SVM) for the diagnosis and early detection of the breast cancer. The sensitivity and specificity of SVM classifier were calculated based on the thermographic images from studies. Furthermore, the heuristic method for SVM's parameters tuning was proposed.
Author Witold Oleszkiewicz ISE
Witold Oleszkiewicz,,
- The Institute of Electronic Systems
, Paweł Cichosz ISE
Paweł Cichosz,,
- The Institute of Electronic Systems
, Dariusz Jagodziński ISE
Dariusz Jagodziński,,
- The Institute of Electronic Systems
, Mateusz Matysiewicz
Mateusz Matysiewicz,,
-
, Łukasz Neumann ISE
Łukasz Neumann,,
- The Institute of Electronic Systems
, Robert Marek Nowak ISE
Robert Marek Nowak,,
- The Institute of Electronic Systems
, Rafał Okuniewski ISE
Rafał Okuniewski,,
- The Institute of Electronic Systems
Pages100312T-7-1-100312T-7-
Publication size in sheets0.5
Book Romaniuk Ryszard (eds.): Proc. SPIE. 10031, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, vol. 10031, 2016, SPIE , ISBN 9781510604858, [781510604865 (electronic) ], 1170 p., DOI:10.1117/12.2257157
Keywords in EnglishBreast cancer ; Image classification ; Machine learning ; Medical diagnostics ; Source mask optimization ; Algorithms
DOIDOI:10.1117/12.2249063
URL http://dx.doi.org/10.1117/12.2249063
Languageen angielski
File
100312T_Cichosz.pdf 338.36 KB
Score (nominal)15
ScoreMinisterial score = 15.0, 27-03-2017, BookChapterMatConf
Ministerial score (2013-2016) = 15.0, 27-03-2017, BookChapterMatConf
Citation count*8 (2018-06-20)
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