Acne detection by ensemble neural networks

• Main Authors: Zhang, Hang, Ma, Tianyi
• Other Authors: School of Materials Science and Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Materials; Acne Detection; Ensemble Model
• Online Access: https://hdl.handle.net/10356/167023
• Institution: Nanyang Technological University

Acne detection, utilizing prior knowledge to diagnose acne severity, number or position through facial images, plays a very important role in medical diagnoses and treatment for patients with skin problems. Recently, deep learning algorithms were introduced in acne detection to improve detection precision. However, it remains challenging to diagnose acne based on the facial images of patients due to the complex context and special application scenarios. Here, we provide an ensemble neural network composed of two modules: (1) a classification module aiming to calculate the acne severity and number; (2) a localization module aiming to calculate the detection boxes. This ensemble model could precisely predict the acne severity, number, and position simultaneously, and could be an effective tool to help the patient self-test and assist the doctor in the diagnosis.