28 September 2016 Application of SVM classifier in thermographic image classification for early detection of breast cancer
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Proceedings Volume 10031, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016; 100312T (2016) https://doi.org/10.1117/12.2249063
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, 2016, Wilga, Poland
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.
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Witold Oleszkiewicz, Paweł Cichosz, Dariusz Jagodziński, Mateusz Matysiewicz, Łukasz Neumann, Robert M. Nowak, Rafał Okuniewski, "Application of SVM classifier in thermographic image classification for early detection of breast cancer", Proc. SPIE 10031, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, 100312T (28 September 2016); doi: 10.1117/12.2249063; https://doi.org/10.1117/12.2249063
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