GAN-Based Approaches for Diagnosis and Imaging of Human Fungal Infections

Human fungal infections are increasingly affecting people worldwide, especially those with weakened immune systems. These infections can range from mild skin conditions to serious life-threatening diseases involving the lungs, brain, or bloodstream. Diagnosing fungal infections at an early stage is often difficult because their symptoms are similar to other infections and many fungal species appear alike in medical images. In addition, limited availability of well-annotated clinical data further complicates accurate diagnosis. Recent advances in artificial intelligence have created new opportunities to improve medical diagnosis. Deep learning methods, particularly Generative Adversarial Networks (GANs), have shown strong potential in medical imaging applications. GANs can generate realistic synthetic images, enhance image quality, and help overcome data scarcity problems commonly seen in fungal infection research. By improving image analysis and supporting automated diagnosis, GAN-based approaches offer a promising pathway to enhance clinical decision-making and advance research in human fungal infections.