Background and objective: There are many types of skin cancer, and melanoma is the most lethal one. Dermoscopy is an important imaging technique to screen melanoma and other skin lesions. However, Skin lesion classification based on computer-aided diagnostic techniques is a challenging task owing to the scarcity of labeled data and class-imbalanced dataset. It is necessary to apply data augmentation technique based on generative adversarial networks (GANs) to skin lesion classification for helping dermatologists in more accurate diagnostic decisions.

Methods: A whole process of using GAN-based data augmentation technology to improve the skin lesion classification performance has been established in this article. First of all, the skin lesion style-based GANs is proposed according to the basic architecture of style-based GANs. The proposed model modifies the structure of style control and noise input in the original generator, adjusts both the generator and discriminator to efficiently synthesize high-quality skin lesion images. As for image classification, the classifier is constructed on the pretrained deep neural network using transfer learning method. The synthetic images from the proposed skin lesion style-based GANs are finally added to the training set to help train the classifier for better classification performance.

Results: The proposed skin lesion style-based GAN has been evaluated by Inception Score (IS), Fréchet Inception Distance (FID), Precision and Recall, and is superior to other compared GAN models in these quantitative evaluation metrics. By adding the synthesized images to the training set, the main classification indicators like accuracy, sensitivity, specificity, average precision and balanced multiclass accuracy are 95.2%, 83.2%, 74.3%, 96.6% and 83.1% on the dataset of International Skin Imaging Collaboration (ISIC) 2018 Challenge, which have been improved by 1.6%, 24.4%, 3.6%, 23.2% and 5.6% respectively compared to the CNN model.

Conclusions: The proposed skin lesion style-based GANs can generate high-quality skin lesion images efficiently, leading to the performance improvement of the classification model. This work provides a valuable reference for medical image analysis based on deep learning.