CycleGAN Based Data Augmentation For Melanoma images Classification

Abstract

It is widely-known that melanoma is one of the deadliest skin cancers with a very high mortality rate, while it is curable with early identification. Therefore, early detection of melanoma is extremely necessary for the treatment of this disease. In recent decades, Convolutional Neural Networks (CNN) have achieved state-of-the-art performance in many different visual classification tasks, so they have also been employed in melanoma recognition tasks. Due to the complexity of the deep learning model and huge numbers of parameters, a large amount of labelled data is required to achieve a better training performance. However, in practical settings, it is difficult for many applications to obtain enough labelled sample data. This paper explore to solve this problems based on data augmentation strategy. In the experiment conducted in our paper, the training data is augmented through CycleGAN-based approaches to generate more training samples with detailed information, and then the CNN model can be trained using the artificially enlarged dataset. The experimental results show that the combination of CycleGAN data augmentation method and EfficientNet B1 can effectively saves the cost of manual annotation, while dramatically improves classification accuracy.