Skin cancer reorganization and classification with deep neural network
  As one kind of skin cancer, melanoma is very dangerous. Dermoscopy based
early detection and recarbonization 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 very high skin lesion segmentation and melanoma diagnosis accuracy: 1)
we build a segmentation neural network (skin_segnn), which achieved very high
lesion boundary detection accuracy; 2) We build another very deep neural
network based on Google inception v3 network (skin_recnn) and its well-trained
weight. The novel designed transfer learning based deep neural network
skin_inceptions_v3_nn helps to achieve a high prediction accuracy.
