Paper Title
Deep Learning-Based Diagnosis for Skin Disease Prediction

Abstract
Since skin disorders are growing more common in the modern world, it is difficult to make reliable predictions about them. Despite its widespread application in the prediction of skin conditions, Generative Adversarial Networks (GANs) have significant drawbacks. Because GANs rely on creating synthetic data, they may produce less accurate findings and frequently experience stability problems during training. We suggest using a U-Net algorithm as a more successful substitute for skin disease prediction in order to get around these issues. Known for its powerful segmentation skills, U-Net is excellent at extracting complex information from medical pictures. By focusing on specific features of skin scans, our method improves prediction accuracy and produces a more trustworthy diagnostic tool. This method seeks to enhance the early identification and accurate classification of skin by utilizing U-Net. Keywords - Skin diseases, Generative Adversarial Networks (GANs), U-Net.