ClearFace: Facial Acne Detection and Classification System Using YOLOv11 and EfficientNet-B0 - Viana, Thierrir Alencar da S.; Gonçalves, Ismael Henrique; Paula, Isaías Silva de; Silva, Francisco Hércules dos S.; Rebouças Filho, Pedro P. (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI), 2025)
Facial acne is a highly prevalent dermatological condition that significantly affects individuals’ physical and psychological well-being. Although clinical diagnosis remains the gold standard, limited access to specialized care highlights the need for scalable, automated solutions. This study presents a deep learning-based system for the detection and classification of facial acne lesions, combining object detection and image classification techniques. The YOLOv11-m model was employed to localize lesions in facial images, followed by classification using an EfficientNet-B0 network. To train and evaluate the system, the Acne21 dataset was adapted to a two-stage pipeline: bounding boxes were used for detection, while cropped regions were labeled into six classes for classification. The system generates lesion counts and calculates the Investigator’s Global Assessment (IGA) severity score based on clinical weights. Experimental results demonstrate the model’s effectiveness in detecting and categorizing different lesion types, offering a promising tool for preliminary acne self-assessment in mobile health applications. This approach contributes to the advancement of accessible dermatological care through AI-driven technologies.