  Skin cancer is one of the most common and deadliest types of cancer. Early
diagnosis of skin cancer at a benign stage is critical to reducing cancer
mortality. To detect skin cancer at an earlier stage an automated system is
compulsory that can save the life of many patients. Many previous studies have
addressed the problem of skin cancer diagnosis using various deep learning and
transfer learning models. However, existing literature has limitations in its
accuracy and time-consuming procedure. In this work, we applied five different
pre-trained transfer learning approaches for binary classification of skin
cancer detection at benign and malignant stages. To increase the accuracy of
these models we fine-tune different layers and activation functions. We used a
publicly available ISIC dataset to evaluate transfer learning approaches. For
model stability, data augmentation techniques are applied to improve the
randomness of the input dataset. These approaches are evaluated using different
hyperparameters such as batch sizes, epochs, and optimizers. The experimental
results show that the ResNet-50 model provides an accuracy of 0.935, F1-score
of 0.86, and precision of 0.94.
