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

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dc.contributor.author Chamola, Vinay
dc.date.accessioned 2025-01-03T11:02:39Z
dc.date.available 2025-01-03T11:02:39Z
dc.date.issued 2024-07
dc.identifier.uri https://www.sciencedirect.com/science/article/pii/S0957417423035583
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16704
dc.description.abstract Skin 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.iso en en_US
dc.publisher Elsevier en_US
dc.subject EEE en_US
dc.subject Skin lesion en_US
dc.subject Image segmentation en_US
dc.subject Classification en_US
dc.subject Deep Learning (DL) en_US
dc.subject Convolution neural network en_US
dc.subject MobileNet en_US
dc.title A novel end-to-end deep convolutional neural network based skin lesion classification framework en_US
dc.type Article en_US


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