AC-Skin: Facial Acne Detection-based on Intelligent Learning Over Large Data Collection via Internet of Things (IoT) for Smart Skincare

Deep learning method analysis is one of the powerful tools that helps in complex environments while detecting and recognizing facial features and extracting prior knowledge, which is required for biomedical investigation. Artificial intelligence is considered an extremely hot research area of computing technology in the current era. There are currently certain issues with this technology’s suitability for autonomous medical data collection. The Internet of Things (IoT) plays a crucial role in this process; nevertheless, a successful integration has become increasingly difficult in light of the recent use of deep learning in skin diagnostics, particularly with regard to the effects of face acne and its severity, which has created a new paradigm in the field of dermatology. This study addresses the classification of face acne using artificial intelligence (AI)-enabled deep learning techniques, particularly convolutional neural networks (CNN). The goal is to summarize the features of the severity and condition of skin damage. This study also discusses the process of gathering enormous amounts of data from various Internet sources by connecting the Internet of Things (IoT) devices, like ESP 32-connected cameras that gather multimedia data in real-time. This work offers a new paradigm for intelligent learning in terms of the identification and detection of facial acne as well as the degree of skin severity metrics through the use of CNN and IoT. This research describes a close loop architecture design called AC-Skin, a self-learning infrastructure solution that primarily concentrates on the essential elements and influencing factors of illness diagnosis for advancements in smart skincare.