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Classification of Dermatological Asymmetry of the Skin Lesions Using Pretrained Convolutional Neural Networks

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Artificial Intelligence and Soft Computing (ICAISC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12855))

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Abstract

In dermatology, malignant melanoma is one the most deadly forms of skin cancer. It is extremely important to detect it at an early stage. One of the methods of detecting it is an evaluation based on dermoscopy combined with one of the criteria for assessing a skin lesion. Such an evaluation method is the Three-Point Checklist of Dermoscopy which is considered a sufficient screening method for the assessment of skin lesions. The proposed method, founded on the convolutional neural networks, is aimed at improving diagnostics and enabling the preliminary assessment of skin lesions by a family doctor. The current paper presents the results of the application of convolutional neural networks: VGG19, Xception, Inception-ResNet-v2, for the assessment of skin lesions asymmetry, along with various variations of the PH2 database. For the best CNN network, we achieved the following results: true positive rate for the asymmetry 92.31%, weighted accuracy 67.41%, F1 score 0.646 and Matthews correlation coefficient 0.533.

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Authors and Affiliations

  1. Faculty of Physics and Applied Informatics, Department of Computer Science, University of Lodz, Pomorska Street 149/153, 90-236, Lodz, Poland

    Michał Beczkowski & Piotr Milczarski

  2. Faculty of Physics and Applied Informatics, Department of Nuclear Physics, and Radiation Safety, University of Lodz, Pomorska Street 149/153, 90-236, Lodz, Poland

    Norbert Borowski

Authors
  1. Michał Beczkowski
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  2. Norbert Borowski
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  3. Piotr Milczarski
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Corresponding author

Correspondence to Piotr Milczarski .

Editor information

Editors and Affiliations

  1. Czestochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Beczkowski, M., Borowski, N., Milczarski, P. (2021). Classification of Dermatological Asymmetry of the Skin Lesions Using Pretrained Convolutional Neural Networks. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12855. Springer, Cham. https://doi.org/10.1007/978-3-030-87897-9_1

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  • DOI: https://doi.org/10.1007/978-3-030-87897-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87896-2

  • Online ISBN: 978-3-030-87897-9

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