Leveraging Deep Learning to Address Diagnostic Challenges with Insufficient Image Data.

ABSTRACT: In recent AI-driven disease diagnosis, the success of models has depended mainly on extensive data sets and advanced algorithms. However, creating traditional data sets for rare or emerging diseases presents significant challenges. To address this issue, this study introduces a direct-self-attention Wasserstein generative adversarial network (DSAWGAN) designed to improve diagnostic capabilities in infectious diseases with limited data availability. DSAWGAN enhances convergence speed, stability, and image quality by integrating attention modules and leveraging the Wasserstein distance optimization. We compared DSAWGAN-generated images with traditional data augmentation and other image generation techniques, evaluating their effectiveness using classification neural networks for diagnostic a

SUBMITTER: Lu JM 

PROVIDER: S-EPMC12481554 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature