Survey of detection and identification of black skin diseases based on machine learning
Publication Type
Conference Proceeding
Publication Date (Issue Year)
2023
Journal Name
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 499 LNICST
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.
Keywords
Survey of Detection, dentification, Black Skin Diseases, Machine Learning
Rsif Scholar Name
Merveille Santi Kpêtchéhoué Zinsou
Thematic Area
ICTs Including Big Data and Artificial Intelligence
Africa Host University (AHU)
University of Gaston Berger (UGB), Senegal
Recommended Citation
Zinsou, 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. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 499 LNICST, 268-284. https://doi.org/10.1007/978-3-031-34896-9_16