Automated Median Nerve Detection in Carpal Tunnel Ultrasound Using Improved YOLO-Based Model
學年 114
學期 2
發表日期 2026-05-27
作品名稱 Automated Median Nerve Detection in Carpal Tunnel Ultrasound Using Improved YOLO-Based Model
作品名稱（其他語言）
著者 Hsin-Yun Hsieh；Yi-Shiung Horng；Chii-Jen Chen
作品所屬單位
出版者
會議名稱 The International Conference on Recent Advancements in Computing in AI, IoT and Computer Engineering Technology (CICET 2026)
會議地點 New Taipei, Taiwan
摘要 This study proposes a deep learning framework that integrates YOLO11 with an improved segmentation model for the automated detection and segmentation of the median nerve cross-sectional area (CSA) in carpal tunnel ultrasound images. To address the subjectivity and inefficiency of manual clinical analysis, the proposed approach aims to enhance the objectivity and consistency of the diagnostic process. Preliminary experimental results show that the method can be applied to the detection and segmentation of the median nerve, demonstrating its potential as a research direction for clinical application in the diagnosis of Carpal Tunnel Syndrome (CTS).
關鍵字 Carpal Tunnel Syndrome, Median nerve, Deep Learning, YOLO.
語言 en_US
收錄於
會議性質 國際
校內研討會地點 淡水校園
研討會時間 20260527~20260529
通訊作者 Chii-Jen Chen
國別 TWN
公開徵稿
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出處