الفهرس 
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Abstract
While recent Years have Witnessed the remarkable Success of deep learning methods automated skin lesion detection Systems, there still exists a gap between manual assessment of experts and automated evaluation of computers. The reason behind such a gap is the deep learning models demand considerable amounts of data, While the availability of annotated images is often limited.
Data Augmentation (DA) is one way to mitigate the lace of labeled
data; however, the augmented images intrinsically have a similar distribution to the original ones, leading to limited performance improvement.
To satisfy the data lake in the real images distribution, we synthesize skin lesion images-realistic but completely different from the original ones-using Generative Adversarial Networks (GANs).