DSpace logo

Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16704
Full metadata record
DC FieldValueLanguage
dc.contributor.authorChamola, Vinay-
dc.date.accessioned2025-01-03T11:02:39Z-
dc.date.available2025-01-03T11:02:39Z-
dc.date.issued2024-07-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0957417423035583-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16704-
dc.description.abstractSkin diseases are reported to contribute 1.79% of the global burden of disease. The accurate diagnosis of specific skin diseases is known to be a challenging task due, in part, to variations in skin tone, texture, body hair, etc. Classification of skin lesions using machine learning is a demanding task, due to the varying shapes, sizes, colors, and vague boundaries of some lesions. The use of deep learning for the classification of skin lesion images has been shown to help diagnose the disease at its early stages. Recent studies have demonstrated that these models perform well in skin detection tasks, with high accuracy and efficiency.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectEEEen_US
dc.subjectSkin lesionen_US
dc.subjectImage segmentationen_US
dc.subjectClassificationen_US
dc.subjectDeep Learning (DL)en_US
dc.subjectConvolution neural networken_US
dc.subjectMobileNeten_US
dc.titleA novel end-to-end deep convolutional neural network based skin lesion classification frameworken_US
dc.typeArticleen_US
Appears in Collections:Department of Electrical and Electronics Engineering

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.