<span>Skin cancer is one of the most dangerous types cancer. Some this lead to death, so must be discovered and indexed avoid its spread through initial detection in impulsive stage. This paper deals with indexing different melanomas using an artificial neural network (ANN) depending on international skin imaging collaboration (ISIC) 2018 dataset that was used. The pre-processing importan...

Skin cancer is a serious health hazard for human society. This disease developed when the pigments that produce skin color become cancerous. Dermatologists face difficulties in diagnosing since many colors seem identical. As result, early diagnosis of lesions (the foundation cancer) very crucial and beneficial totally curing patients. Significant progress has been made creating automated method...

Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets of dermatoscopic images. We tackle this problem by releasing the HAM10000 (“Human Against Machine with 10000 training images”) dataset. We collected dermatoscopic images from different populations acquired and stored by different modalities. Giv...

As one kind of skin cancer, melanoma is very dangerous. Dermoscopy based early detection and recognization strategy is critical for melanoma therapy. However, well-trained dermatologists dominant the diagnostic accuracy. In order to solve this problem, many effort focus on developing automatic image analysis systems. Here we report a novel strategy based on deep learning technique, and achieve ...

With the increasing incidence of severe skin diseases, such as cancer, endoscopic medical imaging has become urgent for revealing internal and hidden tissues under skin. Diagnostic information to help doctors make an accurate diagnosis is provided by endoscopy devices. Nonetheless, most diseases have similar features, which it challenging dermatologists diagnose patients accurately. Therefore, ...

We present a method for skin lesion segmentation for the ISIC 2017 Skin Lesion Segmentation Challenge. Our approach is based on a Fully Convolutional Neural Network architecture which is trained end to end, from scratch, on a small dataset. Our semantic segmentation architecture utilizes several recent innovations in deep learning particularly in the combined use of (i) atrous convolutions to i...