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Artificial Neural Networks and Principal Components Analysis for Detection of Idiopathic Pulmonary Fibrosis in Microscopy Images

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Engineering Applications of Neural Networks (EANN 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 383))

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Abstract

In this study we present a computer assisted image identification and recognition tool that aims to help the diagnosis of idiopathic pulmonary fibrosis in microscopy images. To this end, we use principal components analysis to reduce the dimensionality of the data and subsequently we perform classification using Artificial Neural Networks. The proposed approach succeeded in locating the pathological regions and achieved high quality results in terms of classification accuracy.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Biomedical Informatics, University of Central Greece, Papassiopoulou 2–4, Lamia, 35100, Greece

    Spiros V. Georgakopoulos, Sotiris K. Tasoulis & Vassilis P. Plagianakos

  2. Department of Digital Systems, University of Piraeus, Grigoriou Lampraki 126, Piraeus, 18532, Greece

    Ilias Maglogiannis

Authors
  1. Spiros V. Georgakopoulos
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  2. Sotiris K. Tasoulis
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  3. Vassilis P. Plagianakos
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  4. Ilias Maglogiannis
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Editor information

Editors and Affiliations

  1. Department of Forestry & Management of the Environment and Natural Resources, Democritus University of Thrace, GR-68200, Orestiada, Hellas

    Lazaros Iliadis

  2. Frederick University of Cyprus, Cyprus

    Harris Papadopoulos

  3. Faculty of Engineering and Computing, Coventry University, UK

    Chrisina Jayne

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Georgakopoulos, S.V., Tasoulis, S.K., Plagianakos, V.P., Maglogiannis, I. (2013). Artificial Neural Networks and Principal Components Analysis for Detection of Idiopathic Pulmonary Fibrosis in Microscopy Images. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_30

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  • DOI: https://doi.org/10.1007/978-3-642-41013-0_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41012-3

  • Online ISBN: 978-3-642-41013-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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