A smart facial acne disease monitoring for automate severity assessment using AI-enabled cloud-based internet of things

Abstract

One of the emerging paradigms in the diagnosis and severity assessment of skin disorders, particularly acne, on the face is the use of advanced digital technology (ADT) in skin disease monitoring. It is said to be a very prevalent issue that has to be looked at by experts in this day and age. Nonetheless, the traditional approach to acne diagnosis still relies on the opinions and expertise of medical professionals. There could be fatal outcomes from both delayed and inaccurate diagnoses. Since acne is a condition that directly affects the healthcare system, this study focusses on accelerating the diagnostic process and closing the gap between diagnosis and treatment. In this work, we introduce a smart face acne disease level monitoring device that allows acne sufferers in different geographical locations to track the severity and specifics of their acne and to communicate precautions. Convolutional neural networks, or CNNs, play a major role in this suggested architecture's AI-enabled cloud-based IoT device interconnectivity. Based on a set of photos, CNNs predict the degree of face acne, which could have implications for further study. This proposed study also addresses the influence of age. Geographically speaking, the architecture provides all the domains of skin diagnostic and preventive scheme, notably for acne diagnosis, addressing the present issue faced by patients with limited or no access to e-healthcare services.