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Transfer Learning from ImageNet to the Domain of Pigmented Nevi

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Artificial Intelligence and Soft Computing (ICAISC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14125))

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Abstract

The transfer learning method enables the use of a pretrained convolutional network to efficiently model a secondary domain with less data. In this article 18 public convolutional networks of different architecture and depth, pretrained on ImageNet, are tested on three optimizers (Adam, Rmsprop and SGDM), ten learning rate values and two diverse data sets (ISIC 2017 and Melanoma-ML), to choose the best one for the malignant melanoma vs. atypical (but benign) nevi classification. This is important since both types of the pigmented skin lesions can be visually very similar and difficult to distinguish. For the well-known ISIC 2017 data set, we found the best accuracy of 94.36 ± 1.66% for the ResNet 101 convolutional network with the SGDM optimizer and the learning rate of 6e-4. We show our results against the literature on the subject. The best pretrained model(s) can be easily implemented in dermoscopy systems/applications to assist skin/general physicians of all levels of training and experience and patients for premedical self-examination.

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Author information

Authors and Affiliations

  1. Department of Physics, Astronomy and Applied Computer Science, Jagiellonian University, ul. Łojasiewicza 11, 30-348, Krakow, Poland

    Grzegorz Surówka

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  1. Grzegorz Surówka
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Correspondence to Grzegorz Surówka .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Krakow, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Surówka, G. (2023). Transfer Learning from ImageNet to the Domain of Pigmented Nevi. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2023. Lecture Notes in Computer Science(), vol 14125. Springer, Cham. https://doi.org/10.1007/978-3-031-42505-9_23

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  • DOI: https://doi.org/10.1007/978-3-031-42505-9_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42504-2

  • Online ISBN: 978-3-031-42505-9

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