  Skin cancer is one of the deadly types of cancer and is common in the world.
Recently, there has been a huge jump in the rate of people getting skin cancer.
For this reason, the number of studies on skin cancer classification with deep
learning are increasing day by day. For the growth of work in this area, the
International Skin Imaging Collaboration (ISIC) organization was established
and they created an open dataset archive. In this study, images were taken from
ISIC 2017 Challenge. The skin cancer images taken were preprocessed and data
augmented. Later, these images were trained with transfer learning and
fine-tuning approach and deep learning models were created in this way. 3
different mobile deep learning models and 3 different batch size values were
determined for each, and a total of 9 models were created. Among these models,
the NASNetMobile model with 16 batch size got the best result. The accuracy
value of this model is 82.00%, the precision value is 81.77% and the F1 score
value is 0.8038. Our method is to benchmark mobile deep learning models which
have few parameters and compare the results of the models.
