A GAN-Based Data Augmentation Method for Imbalanced Multi-Class Skin Lesion Classification
A GAN-Based Data Augmentation Method for Imbalanced Multi-Class Skin Lesion Classification is a scholarly work, published in 2024 in ''IEEE Access''. The main subjects of the publication include data augmentation, data mining, artificial intelligence, class, pattern recognition, and computer science. The paper proposes a two-stage GAN-based method to synthesize fine-grained and diverse $256 \times 256$ pixels skin lesion images for the imbalanced dataset, named Self-Transfer GAN (STGAN).