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From Pixels to Diagnosis: AI-Driven Skin Lesion Recognition

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Advances in Smart Healthcare Paradigms and Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 244))

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Abstract

Skin cancer is a serious public health problem with a sharply increasing incidence in recent years, which has a major impact on quality of life and can be disfiguring or even fatal. Deep learning techniques can be used to analyze dermoscopic images, resulting in automated systems that can improve the clinical confidence of the diagnosis – also avoiding unnecessary surgery – help clinicians objectively communicate its outcome, reduce errors related to human fatigue, and cut costs affecting the health system. In this chapter, we present an entire pipeline to analyze skin lesion images in order to distinguish nevi from melanomas, also integrating patient clinical data to reach a diagnosis. Furthermore, to make our artificial intelligence tool explainable for both clinicians and patients, dermoscopic images are further processed to obtain their segmented counterparts, where the lesion contour is easily observable, and saliency maps, highlighting the areas of the lesion that prompted the classifier to make its decision. Experimental results are promising and have been positively evaluated by human experts.

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Notes

  1. 1.

    https://challenge.isic-archive.com/task/49/.

  2. 2.

    Publicy available at https://simonebonechi.github.io/downloads/isic_wsm.

  3. 3.

    In May 2018, a uniform data law was approved for all 27 EU member states, aimed at protecting the privacy of European citizens on digital infrastructures around the world, called General Data Protection Regulation.

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Author information

Authors and Affiliations

  1. Department of Information Engineering and Mathematics, University of Siena, Siena, Italy

    Monica Bianchini, Paolo Andreini & Simone Bonechi

  2. Department of Social, Political and Cognitive Science, University of Siena, Siena, Italy

    Simone Bonechi

Authors
  1. Monica Bianchini
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  2. Paolo Andreini
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  3. Simone Bonechi
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Corresponding author

Correspondence to Monica Bianchini .

Editor information

Editors and Affiliations

  1. Department of Computational Intelligence, Wrocla Universtity of Science and Technology, Wroclaw, Poland

    Halina Kwaśnicka

  2. Inc., The Permanente Medical Group, California, CA, USA

    Nikhil Jain

  3. Department of Computational Intelligence, Wrocla Universtity, Wroclaw, Poland

    Urszula Markowska-Kaczmar

  4. Deakin University, Victoria, VIC, Australia

    Chee Peng Lim

  5. KES International, Selby, UK

    Lakhmi C. Jain

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Bianchini, M., Andreini, P., Bonechi, S. (2023). From Pixels to Diagnosis: AI-Driven Skin Lesion Recognition. In: Kwaśnicka, H., Jain, N., Markowska-Kaczmar, U., Lim, C.P., Jain, L.C. (eds) Advances in Smart Healthcare Paradigms and Applications. Intelligent Systems Reference Library, vol 244. Springer, Cham. https://doi.org/10.1007/978-3-031-37306-0_6

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