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Skin Cancer Automatic Detection Based on Image Characteristics of Shape, Colour, and Texture

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Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020) (SoCPaR 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1383))

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Abstract

Skin cancer is the most common type of cancer. Melanoma is a malign type of skin cancer responsible for several deaths in the world. An early diagnosis increases the probability of cure. Dermatoscopy is one of the procedures for such diagnosis. Using dermatoscopy, it is possible to recognize skin structures that are not visible to the human sight. The dermatoscope zooms in images for a more precise diagnostic. Although less efficient than the analysis of experts, it is possible to find several proposals for the automatic classification of skin lesions in the literature, some of them using image characteristics of shape, colour, and texture. Our proposal aims at supporting doctors using dermatoscopy for the automatic classification of skin lesions. In our experiments, we used 21 classification algorithms with different inducing paradigms. The best results were obtained using the FuzzyDT algorithm, based on a set of image characteristics related to shape, colour, and texture. One of the main contributions of this proposal, is the characteristic selection procedure using FuzzyDT, which obtained high accuracy, as well as a dataset of preprocessed cancer skin images. The proposal is detailed and the results are presented and discussed.

We thank CAPES for the financial support.

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Author information

Authors and Affiliations

  1. Federal Institute of Rio Grande do Norte, Mossoró, Brazil

    Hugo W. Maia

  2. The Brazilian University of the Semiarid Region, Mossoró, Brazil

    Marcos E. Cintra

Authors
  1. Hugo W. Maia
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  2. Marcos E. Cintra
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Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Systems Innovation, School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo, Japan

    Yukio Ohsawa

  3. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  4. Vardhaman College of Engineering, Hyderabad, Telangana, India

    M.A. Jabbar

  5. Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  6. Queen’s University, Belfast, UK

    Seán McLoone

  7. Northumbria University, Newcastle, UK

    Biju Issac

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Maia, H.W., Cintra, M.E. (2021). Skin Cancer Automatic Detection Based on Image Characteristics of Shape, Colour, and Texture. In: Abraham, A., et al. Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020). SoCPaR 2020. Advances in Intelligent Systems and Computing, vol 1383. Springer, Cham. https://doi.org/10.1007/978-3-030-73689-7_6

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