Please use this identifier to cite or link to this item: https://repository.rsif-paset.org/xmlui/handle/123456789/295
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dc.contributor.authorZinsou, K. Merveille Santi-
dc.contributor.authorDiop, Idy-
dc.contributor.authorDiop, Cheikh Talibouya-
dc.contributor.authorBah, Alassane-
dc.contributor.authorNdiaye, Maodo-
dc.contributor.authorSow, Doudou-
dc.date.accessioned2023-10-31T11:01:04Z-
dc.date.available2023-10-31T11:01:04Z-
dc.date.issued2023-06-30-
dc.identifier.urihttps://repository.rsif-paset.org/xmlui/handle/123456789/295-
dc.descriptionFull text: https://doi.org/10.1007/978-3-031-34896-9_16en_US
dc.description.abstractDue 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.en_US
dc.publisherSpringer Linken_US
dc.subjectBlack skin diseases, CNN, transfer learning, Deep learning, Machine learningen_US
dc.titleSurvey of Detection and Identification of Black Skin Diseases Based on Machine Learningen_US
dc.typeArticleen_US
Appears in Collections:ICTs including Big Data and Artificial Intelligence

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