报告详情
Smart Mobile Application for Automated Detection of Skin Diseases and Disorders Using an Ensemble of Yolov8, Yolo-Nas, and Efficientdet Models
编号：80
访问权限：仅限参会人
更新：2025-12-21 13:01:01 浏览：21次
拓展类型2
报告开始：暂无开始时间（Asia/Amman）
报告时间：暂无持续时间
所在会场：[暂无会议] [暂无会议段]
暂无文件
摘要
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.
关键词
Skin disease detection,,skin disease detection,Medical image analysis,Object detection,Artificial Intel,Artificial Intelligence,Ensemble Models
报告人
Matthew Ethan Israel
Student Researcher, University of San Carlos稿件作者
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重要日期
-
会议日期
12月29日
2025
至12月31日
2025
-
12月30日 2025
报告提交截止日期
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12月30日 2025
注册截止日期
-
12月31日 2025
初稿截稿日期
主办单位
国际科学联合会
承办单位
扎尔卡大学
联系方式
- Miss AsianComNet
- as******@usssociety.org
历届会议
-
2024年10月24日 泰国 Bangkok
2024亚洲通讯与网络会议
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