Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposes.

Journal: Archives of dermatological research
Published Date: Mar 6, 2024

Abstract

This paper presents the most current and innovative solutions applying modern digital image processing methods for the purpose of skin cancer diagnostics. Skin cancer is one of the most common types of cancers. It is said that in the USA only, one in five people will develop skin cancer and this trend is constantly increasing. Implementation of new, non-invasive methods plays a crucial role in both identification and prevention of skin cancer occurrence. Early diagnosis and treatment are needed in order to decrease the number of deaths due to this disease. This paper also contains some information regarding the most common skin cancer types, mortality and epidemiological data for Poland, Europe, Canada and the USA. It also covers the most efficient and modern image recognition methods based on the artificial intelligence applied currently for diagnostics purposes. In this work, both professional, sophisticated as well as inexpensive solutions were presented. This paper is a review paper and covers the period of 2017 and 2022 when it comes to solutions and statistics. The authors decided to focus on the latest data, mostly due to the rapid technology development and increased number of new methods, which positively affects diagnosis and prognosis.

Authors

  • Maria Myslicka
    Faculty of Medicine, Wroclaw Medical University, J. Mikulicza-Radeckiego 5, 50-345, Wroclaw, Poland. mariamyslicka38@gmail.com.
  • Aleksandra Kawala-Sterniuk
    Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758, Opole, Poland. kawala84@gmail.com.
  • Anna Bryniarska
    Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758, Opole, Poland.
  • Adam Sudol
    Faculty of Natural Sciences and Technology, University of Opole, Dmowskiego 7-9, 45-368, Opole, Poland.
  • Michal Podpora
    Department of Computer Science, Opole University of Technology, Proszkowska 76, 45-758 Opole, Poland.
  • Rafal Gasz
    Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758, Opole, Poland.
  • Radek Martinek
    Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 700 30 Ostrava, Czech Republic.
  • Radana Kahankova Vilimkova
    Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758, Opole, Poland.
  • Dominik Vilimek
    Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, FEECS, 708 00 Ostrava-Poruba, Czech Republic.
  • Mariusz Pelc
    Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758, Opole, Poland. m.pelc@po.edu.pl.
  • Dariusz MikoĊ‚ajewski
    Institute of Computer Science, Kazimierz Wielki University, 85-064 Bydgoszcz, Poland.

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