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Skin Lesion Segmentation Based on Improved U-net -Deep learning project

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Skin Lesion Segmentation Based on Improved U-net -Deep learning project

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SKU: Skin Lesion Segmentation Based on Improved U-net Category:

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Skin Lesion Segmentation Based on Improved U-net -Deep learning project

Melanoma is one of the most common and dangerous skin cancers, accounting for 75% of deaths associated with skin cancer. Detection of melanoma in early stages can significantly improve the survival rate. Automatic segmentation of melanoma is an important and essential step for accurate detection of melanoma. Many existing works based on traditional segmentation methods and deep learning methods have been proposed for high-resolution dermoscopy images. However, due to the intrinsic visual complexity and ambiguity among different skin conditions, automatic melanoma segmentation is still a challenging task for existing methods. Among these methods, the deep learning methods have obtained more attention recently due to its high performance by training an end-to-end framework, which needs no human interaction. U-net is a very popular deep learning model for medical image segmentation. In this paper, we propose an efficient skin lesion segmentation based on improved U-net model

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