Deep Acne: Advancing Automated Acne Identification through Deep Neural Networks

Acne vulgaris is a common dermatological illness that impacts a considerable segment of the population, especially during adolescence. Effective acne medication and management rely on the timely and accurate detection of lesions. The classification of skin lesions is a task in medical image analysis where deep learning methodologies have shown promising outcomes in recent years. This research seeks to assess the efficacy of various advanced object detection models, including Faster R-CNN, R-FCN, SDD, and YOLO, for the automated categorization of acne lesions in dermatological images. YOLO emerges as the best-performing model (with 97.3% accuracy) for acne classification based on these results, closely followed by R-FCN. This study contributes to the development of automated acne classification systems, which can assist dermatologists in clinical practice, telemedicine consultations, and large-scale skin disease screening programs.