A novel end-to-end deep convolutional neural network based skin lesion classification framework

dc.contributor.authorChamola, Vinay
dc.date.accessioned2025-01-03T11:02:39Z
dc.date.available2025-01-03T11:02:39Z
dc.date.issued2024-07
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.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0957417423035583
dc.identifier.urihttps://dspace.bits-pilani.ac.in/handle/123456789/16704
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

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