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
AbstractThis 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.
|Publication size in sheets||0.5|
|Book||Romaniuk Ryszard (eds.): Proc. SPIE. 10031, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, vol. 10031, 2016, P.O. Box 10, Bellingham, Washington 98227-0010 USA , SPIE , ISBN 9781510604858, [781510604865 (electronic) ], 1170 p., DOI:10.1117/12.2257157|
|Keywords in English||Breast cancer ; Image classification ; Machine learning ; Medical diagnostics ; Source mask optimization ; Algorithms|
|Score|| = 15.0, 04-03-2020, BookChapterMatConfByConferenceseries|
= 15.0, 04-03-2020, BookChapterMatConfByConferenceseries
|Publication indicators||= 1; = 10; = 10.0|
|Citation count*||10 (2020-08-28)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.