  Skin cancer, the most common human malignancy, is primarily diagnosed
visually by physicians [1]. Classification with an automated method like CNN
[2, 3] shows potential for challenging tasks [1]. By now, the deep
convolutional neural networks are on par with human dermatologist [1]. This
abstract is dedicated on developing a Deep Learning method for ISIC [5] 2017
Skin Lesion Detection Competition hosted at [6] to classify the dermatology
pictures, which is aimed at improving the diagnostic accuracy rate and general
level of the human health. The challenge falls into three sub-challenges,
including Lesion Segmentation, Lesion Dermoscopic Feature Extraction and Lesion
Classification. This project only participates in the Lesion Classification
part. This algorithm is comprised of three steps: (1) original images
preprocessing, (2) modelling the processed images using CNN [2, 3] in Caffe [4]
framework, (3) predicting the test images and calculating the scores that
represent the likelihood of corresponding classification. The models are built
on the source images are using the Caffe [4] framework. The scores in
prediction step are obtained by two different models from the source images.
