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Survey of Detection and Identification of Black Skin Diseases Based on Machine Learning

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Towards new e-Infrastructure and e-Services for Developing Countries (AFRICOMM 2022)

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Abstract

Due to their physical and psychological effects on patients, skin diseases are a major and worrying problem in societies. Early detection of skin diseases plays an important role in treatment. The process of diagnosis and treatment of skin lesions is related to the skills and experience of the medical specialist. The diagnostic procedure must be precise and timely. Recently, the science of artificial intelligence has been used in the field of diagnosis of skin diseases through the use of learning algorithms and exploiting the vast amount of data available in health centers and hospitals. However, although many solutions are proposed for white skin diseases, they are not suitable for black skin. These algorithms fail to identify the range of skin conditions in black skin effectively. The objective of this study is to show that few researchers are interested in developing algorithms for the diagnosis of skin disease in black patients. This is not the case concerning dermatology on white skin for which there is a multitude of solutions for automatic detection.

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Acknowledgments

The authors are thankful for the support provided by the Regional Scholarship and Innovation Fund (RSIF), and the Partnerships for Skills in Applied Sciences, Engineering, and Technologies (PASET).

Funding

This work was supported by the Regional Scholarship and Innovation Fund (RSIF), and the Partnerships for Skills in Applied Sciences, Engineering, and Technologies (PASET).

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Authors and Affiliations

  1. Ummisco-Sénégal, Institut de Recherche pour le Développement (IRD-Hann), Ecole Supérieure Polytechnique (ESP/UCAD), Ecole Doctorale des Sciences et des Technologies, University Gaston Berger of Saint Louis (UGB), BP: 234, Saint-Louis, Senegal

    K. Merveille Santi Zinsou, Idy Diop, Cheikh Talibouya Diop, Alassane Bah, Maodo Ndiaye & Doudou Sow

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  1. K. Merveille Santi Zinsou
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  2. Idy Diop
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  3. Cheikh Talibouya Diop
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  4. Alassane Bah
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  5. Maodo Ndiaye
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  6. Doudou Sow
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Corresponding author

Correspondence to K. Merveille Santi Zinsou .

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Editors and Affiliations

  1. Taif University, Ta'if, Saudi Arabia

    Rashid A. Saeed

  2. State University of Zanzibar, Zanzibar, Tanzania

    Abubakar D. Bakari

  3. State University of Zanzibar, Zanzibar, Tanzania

    Yahya Hamad Sheikh

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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zinsou, K.M.S., Diop, I., Diop, C.T., Bah, A., Ndiaye, M., Sow, D. (2023). Survey of Detection and Identification of Black Skin Diseases Based on Machine Learning. In: Saeed, R.A., Bakari, A.D., Sheikh, Y.H. (eds) Towards new e-Infrastructure and e-Services for Developing Countries. AFRICOMM 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 499. Springer, Cham. https://doi.org/10.1007/978-3-031-34896-9_16

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  • DOI: https://doi.org/10.1007/978-3-031-34896-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34895-2

  • Online ISBN: 978-3-031-34896-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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