Skin lesions are a severe disease globally. Early detection of melanoma in 
dermoscopy images significantly increases the survival rate. However, the 
accurate recognition of melanoma is extremely challenging due to the following 
reasons: low contrast between lesions and skin, visual similarity between 
melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of 
skin tumors is very useful to increase the accuracy and efficiency of 
pathologists. In this paper, we proposed two deep learning methods to address 
three main tasks emerging in the area of skin lesion image processing, i.e., 
lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and 
lesion classification (task 3). A deep learning framework consisting of two 
fully convolutional residual networks (FCRN) is proposed to simultaneously 
produce the segmentation result and the coarse classification result. A lesion 
index calculation unit (LICU) is developed to refine the coarse classification 
results by calculating the distance heat-map. A straight-forward CNN is proposed 
for the dermoscopic feature extraction task. The proposed deep learning 
frameworks were evaluated on the ISIC 2017 dataset. Experimental results show 
the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for 
task 2 and 0.912 for task 3 were achieved.