  Deep learning applications for assessing medical images are limited because
the datasets are often small and imbalanced. The use of synthetic data has been
proposed in the literature, but neither a robust comparison of the different
methods nor generalizability has been reported. Our approach integrates a
retinal image quality assessment model and StyleGAN2 architecture to enhance
Age-related Macular Degeneration (AMD) detection capabilities and improve
generalizability. This work compares ten different Generative Adversarial
Network (GAN) architectures to generate synthetic eye-fundus images with and
without AMD. We combined subsets of three public databases (iChallenge-AMD,
ODIR-2019, and RIADD) to form a single training and test set. We employed the
STARE dataset for external validation, ensuring a comprehensive assessment of
the proposed approach. The results show that StyleGAN2 reached the lowest
Frechet Inception Distance (166.17), and clinicians could not accurately
differentiate between real and synthetic images. ResNet-18 architecture
obtained the best performance with 85% accuracy and outperformed the two human
experts (80% and 75%) in detecting AMD fundus images. The accuracy rates were
82.8% for the test set and 81.3% for the STARE dataset, demonstrating the
model's generalizability. The proposed methodology for synthetic medical image
generation has been validated for robustness and accuracy, with free access to
its code for further research and development in this field.
