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DC Field | Value | Language |
---|---|---|
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 |
Appears in Collections: | Department of Electrical and Electronics Engineering |
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