Research Article
Survey of Detection and Identification of Black Skin Diseases Based on Machine Learning
@INPROCEEDINGS{10.1007/978-3-031-34896-9_16, author={K. Merveille Santi Zinsou and Idy Diop and Cheikh Talibouya Diop and Alassane Bah and Maodo Ndiaye and Doudou Sow}, title={Survey of Detection and Identification of Black Skin Diseases Based on Machine Learning}, proceedings={Towards new e-Infrastructure and e-Services for Developing Countries. 14th EAI International Conference, AFRICOMM 2022, Zanzibar, Tanzania, December 5-7, 2022, Proceedings}, proceedings_a={AFRICOMM}, year={2023}, month={6}, keywords={Black skin diseases CNN transfer learning Deep learning Machine learning}, doi={10.1007/978-3-031-34896-9_16} }- K. Merveille Santi Zinsou
Idy Diop
Cheikh Talibouya Diop
Alassane Bah
Maodo Ndiaye
Doudou Sow
Year: 2023
Survey of Detection and Identification of Black Skin Diseases Based on Machine Learning
AFRICOMM
Springer
DOI: 10.1007/978-3-031-34896-9_16
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.
