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The Influence of the Learning Set on the Evaluation of Microcalcifications Using Artificial Neural Networks

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Mechatronics 2017 (MECHATRONICS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 644))

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Abstract

Neural networks have been devised to detect the location of microcalcifications (segmentation method) to evaluate the degree of malignancy, based on identified features describing potential microcalcifications. Previous studies focused on the selection of the optimal vector of features (set of features), describing the mammogram and evaluation of the MLBP and Fahlman’s neural networks as decision support tools. This article presents next study of applications of the neural network methods for the automatic analysis of mammograms. Methods elaborated for the digitalization of the mammograms are presented earlier.

The study of identification and classification of masses and microcalcifications in digital mammograms are shown. The purpose of this paper is to indicate the representativeness of the dataset on the results obtained using artificial neural networks in the detection of microcalcifications.

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Author information

Authors and Affiliations

  1. Politechnika Warszawska, Wydział Administracji i Nauk Społecznych, Warsaw, Poland

    Krzysztof Urbaniak

  2. Politechnika Warszawska, Wydział Mechatroniki, Warsaw, Poland

    Krzysztof Lewenstein

Authors
  1. Krzysztof Urbaniak
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  2. Krzysztof Lewenstein
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Correspondence to Krzysztof Urbaniak .

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Editors and Affiliations

  1. Faculty of Mechanical Engineering, Institute of Mathematics, Brno University of Technology, Brno, Czech Republic

    Tomáš Březina

  2. Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, Warsaw University of Technology, Warsaw, Poland

    Ryszard Jabłoński

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Urbaniak, K., Lewenstein, K. (2018). The Influence of the Learning Set on the Evaluation of Microcalcifications Using Artificial Neural Networks. In: Březina, T., Jabłoński, R. (eds) Mechatronics 2017. MECHATRONICS 2017. Advances in Intelligent Systems and Computing, vol 644. Springer, Cham. https://doi.org/10.1007/978-3-319-65960-2_68

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  • DOI: https://doi.org/10.1007/978-3-319-65960-2_68

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65959-6

  • Online ISBN: 978-3-319-65960-2

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