Advanced Pigmented Facial Skin Analysis Using Conditional Generative Adversarial Networks

In recent years, artificial intelligence (AI) approaches in computer vision and medical technology have been combined to create various convenient and accurate tools to assist medical treatments. In this work, we propose conditional generative adversarial networks (conditional GANs)-based pigmented facial skin analysis system for melasma diagnosis. In the past, melasma diagnosis was based on subjective diagnoses from doctors, and there were few automatic melasma analysis methods. The proposed system helps to determine the region according to the melasma's severity. Areas associated with melasma and hemoglobin are detected to determine whether they may require special treatments. Furthermore, the proposed work cooperates with HUANGDERM dermatology to collect a facial skin pigmented dataset. We divide the dataset into 3,000 groups for training datasets and 678 groups for testing. Each group contains four categories of images: standard white light, polarized light, melanin and hemoglobin distribution. As a result, the proposed system successfully generates melasma and hemoglobin images and performs well with respect to subjective and objective evaluations.