Matthew Ethan U. Israel, Cecil Raphael S. Quibranza, Christian V. Maderazo
Skin diseases and disorders impact a significant portion of the global population, representing a nonfatal but substantial disease burden. Accurate and timely diagnosis is often challenging, particularly in low-resource settings with little access to specialists. To address this, an image-based skin disease detection system utilizing an ensemble of deep learning models YOLOv8, YOLO-NAS, and EfficientDet was developed. The system classified five common skin conditions acne vulgaris, eczema, melasma, rosacea, and shingles using a publicly available, annotated dataset enhanced by preprocessing and augmentation. Outputs from individual models were reviewed by a dermatologist for clinical reliability. The ensemble-based approach reached high levels of precision, recall, and mean average precision @0.5(mAP@0.5) and mean average precision @0.5:0.95(mAP@0.5:0.95) demonstrating robust performance in screening applications. The solution was successfully deployed as a proof-of-concept mobile application for early detection and support, especially in underserved areas. Ethical considerations regarding data privacy and dataset bias were addressed throughout the study. © 2025 IEEE.
University of San Carlos, Department of Computer, Information Sciences, and Mathematics, Cebu City, Philippines
