Presentation Details
Smart Mobile Application for Automated Detection of Skin Diseases and Disorders Using an Ensemble of Yolov8, Yolo-Nas, and Efficientdet Models
ID：80
View Protection：ATTENDEE
Updated Time：2025-12-21 13:01:01 Hits：457
Online
Start Time：2025-12-30 16:15（Asia/Amman）
Duration：15min
Session：S7 Track 7: Pattern Recognition, Computer Vision and Image Processing »
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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(mAP@0.5), 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.
Keywords
Skin disease detection,,skin disease detection,Medical image analysis,Object detection,Artificial Intel,Artificial Intelligence,Ensemble Models
Speaker
Matthew Ethan Israel
Student Researcher, University of San CarlosSubmission Author
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Important Date
-
Conference Date
Dec 29
2025
toDec 31
2025
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Dec 20 2025
Draft paper submission deadline
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Dec 31 2025
Contribution Submission Deadline
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Dec 31 2025
Registration deadline
Sponsored By
United Societies of Science
Organized By
Zarqa University
Contact Information
- Miss AsianComNet
- as******@usssociety.org
Previous Conferences
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2024-10-24 Thailand Bangkok
2024 Asian Conference on Communication and Networks
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