Smart Mobile Application for Automated Detection of Skin Diseases and Disorders Using an Ensemble of Yolov8, Yolo-Nas, and Efficientdet Models
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Updated time:2025-12-21 13:01:01 Views:195
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Abstract
Abstract—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([email protected]), mean average precision @0.5:0.95([email protected]: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.
Keywords
Skin disease detection,,skin disease detection,Medical image analysis,Object detection,Artificial Intel,Artificial Intelligence,Ensemble Models
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