  Today, skin cancer is considered as one of the most dangerous and common
cancers in the world which demands special attention. Skin cancer may be
developed in different types; including melanoma, actinic keratosis, basal cell
carcinoma, squamous cell carcinoma, and Merkel cell carcinoma. Among them,
melanoma is more unpredictable. Melanoma cancer can be diagnosed at early
stages increasing the possibility of disease treatment. Automatic
classification of skin lesions is a challenging task due to diverse forms and
grades of the disease, demanding the requirement of novel methods
implementation. Deep convolution neural networks (CNN) have shown an excellent
potential for data and image classification. In this article, we inspect skin
lesion classification problem using CNN techniques. Remarkably, we present that
prominent classification accuracy of lesion detection can be obtained by proper
designing and applying of transfer learning framework on pre-trained neural
networks, without any requirement for data enlargement procedures i.e. merging
VGG16 and VGG19 architectures pre-trained by a generic dataset with modified
AlexNet network, and then, fine-tuned by a subject-specific dataset containing
dermatology images. The convolution neural network was trained using 2541
images and, in particular, dropout was used to prevent the network from
overfitting. Finally, the validity of the model was checked by applying the
K-fold cross validation method. The proposed model increased classification
accuracy by 3% (from 94.2% to 98.18%) in comparison with other methods.
