  Computer-assisted diagnosis (CAD) based on deep learning has become a crucial
diagnostic technology in the medical industry, effectively improving diagnosis
accuracy. However, the scarcity of brain tumor Magnetic Resonance (MR) image
datasets causes the low performance of deep learning algorithms. The
distribution of transformed images generated by traditional data augmentation
(DA) intrinsically resembles the original ones, resulting in a limited
performance in terms of generalization ability. This work improves Progressive
Growing of GANs with a structural similarity loss function (PGGAN-SSIM) to
solve image blurriness problems and model collapse. We also explore other
GAN-based data augmentation to demonstrate the effectiveness of the proposed
model. Our results show that PGGAN-SSIM successfully generates 256x256
realistic brain tumor MR images which fill the real image distribution
uncovered by the original dataset. Furthermore, PGGAN-SSIM exceeds other
GAN-based methods, achieving promising performance improvement in Frechet
Inception Distance (FID) and Multi-scale Structural Similarity (MS-SSIM).
