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Proceedings Paper

Application of SVM classifier in thermographic image classification for early detection of breast cancer
Author(s): Witold Oleszkiewicz; Paweł Cichosz; Dariusz Jagodziński; Mateusz Matysiewicz; Łukasz Neumann; Robert M. Nowak; Rafał Okuniewski
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Paper 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.

Paper Details

Date Published: 28 September 2016
PDF: 8 pages
Proc. SPIE 10031, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, 100312T (28 September 2016); doi: 10.1117/12.2249063
Show Author Affiliations
Witold Oleszkiewicz, Warsaw Univ. of Technology (Poland)
Paweł Cichosz, Warsaw Univ. of Technology (Poland)
Dariusz Jagodziński, Warsaw Univ. of Technology (Poland)
Mateusz Matysiewicz, Warsaw Univ. of Technology (Poland)
Łukasz Neumann, Warsaw Univ. of Technology (Poland)
Robert M. Nowak, Warsaw Univ. of Technology (Poland)
Rafał Okuniewski, Warsaw Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 10031:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016
Ryszard S. Romaniuk, Editor(s)

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