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Skin Lesion Diagnosis with Imbalanced ECOC Ensembles

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Machine Learning, Optimization, and Data Science (LOD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12566))

Abstract

Diagnosis of skin lesions is a challenging task due to the similarities between different lesion types, in terms of appearance, location, and size. We present a deep learning method for skin lesion classification by fine-tuning three pre-trained deep learning architectures (Xception, Inception-ResNet-V2, and NasNetLarge), using the training set provided by ISIC2019 organizers. We combine deep convolutional networks with the Error Correcting Output Codes (ECOC) framework to address the open set classification problem and to deal with the heavily imbalanced dataset of ISIC2019. Experimental results show that the proposed framework achieves promising performance that is comparable with the top results obtained in the ISIC2019 challenge leaderboard.

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Notes

  1. 1.

    We consider a dermoscopy as uncropped if one-fourth the number of original pixels are dark.

  2. 2.

    https://challenge2019.isic-archive.com/live-leaderboard.html.

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Author information

Authors and Affiliations

  1. Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey

    Sara Atito Ali Ahmed & Berrin Yanikoglu

  2. Centre for Medical Image Computing (CMIC), University College London, London, UK

    Cemre Zor

  3. Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, Guildford, UK

    Muhammad Awais & Josef Kittler

Authors
  1. Sara Atito Ali Ahmed
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  2. Berrin Yanikoglu
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  3. Cemre Zor
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  4. Muhammad Awais
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  5. Josef Kittler
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Corresponding author

Correspondence to Sara Atito Ali Ahmed .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Giorgio Jansen

  5. Almawave, Rome, Italy

    Vincenzo Sciacca

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Ali Ahmed, S.A., Yanikoglu, B., Zor, C., Awais, M., Kittler, J. (2020). Skin Lesion Diagnosis with Imbalanced ECOC Ensembles. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12566. Springer, Cham. https://doi.org/10.1007/978-3-030-64580-9_25

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  • DOI: https://doi.org/10.1007/978-3-030-64580-9_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64579-3

  • Online ISBN: 978-3-030-64580-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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