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Identification of Eyelid Basal Cell Carcinoma Using Artificial Neural Networks

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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference (EANN 2020)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 2))

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Abstract

First results of the classification of the eyelid Basal Cell Carcinoma using Artificial Neural Networks are presented. Full, or half-face photographs of healthy subjects and patients suffering from eyelid Basal Cell Carcinoma were used to train and validate Artificial Neural Networks for the purpose of pattern recognition, identification and classification. The efficiency of the algorithm was tested using various training methods and it was evaluated using the accuracy score, that is, the ration of the number of the correctly classified cases over the total number of cases under examination. With respect to the accuracy, the proposed algorithm reached up to 100% performance. The algorithm is accompanied by a specifically designed and developed user friendly Graphical User Interface.

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Author information

Authors and Affiliations

  1. Laboratory of Medical Physics, Department of Medicine, Democritus University of Thrace, Komotini, Greece

    Evagelos Georgios Chatzopoulos & Adam Adamopoulos

  2. Laboratory of Medical Informatics, Department of Medicine, Democritus University of Thrace, Komotini, Greece

    George Anastassopoulos

  3. Department of Electrical and Computer Engineering, University of Patras, Patras, Greece

    George Anastassopoulos

  4. Clinic of Ophthalmology, Department of Medicine, University of Crete, Heraklion, Greece

    Efstathios Detorakis

Authors
  1. Evagelos Georgios Chatzopoulos
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  2. George Anastassopoulos
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  3. Efstathios Detorakis
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  4. Adam Adamopoulos
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Corresponding author

Correspondence to Adam Adamopoulos .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Lancaster University, Lancaster, UK

    Plamen Parvanov Angelov

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Chatzopoulos, E.G., Anastassopoulos, G., Detorakis, E., Adamopoulos, A. (2020). Identification of Eyelid Basal Cell Carcinoma Using Artificial Neural Networks. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_30

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  • DOI: https://doi.org/10.1007/978-3-030-48791-1_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48790-4

  • Online ISBN: 978-3-030-48791-1

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