期刊論文
| 學年 | 114 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2025-12-26 |
| 作品名稱 | Pediatric Wrist Fracture Detection Using Feature Context Excitation Modules in X‐ray Images |
| 作品名稱（其他語言） | |
| 著者 | Rui‐Yang Ju; Chun‐Tse Chien; Enkaer Xieerke; Jen‐Shiun Chiang |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | IET Image Processing 20(1), e70269 |
| 摘要 | Children often suffer wrist trauma in daily life and typically require radiologists to analyse and interpret X-ray images before undergoing surgical treatment. The development of deep learning has enabled neural networks to serve as computer-aided diagnosis (CAD) tools, assisting doctors and experts in medical image diagnostics. Since the you only look once version-8 (YOLOv8) model has achieved satisfactory success in object detection tasks, it has been applied to various fracture detection tasks. This work introduces four variants of feature contexts excitation-YOLOv8 (FCE-YOLOv8) model, each incorporating a different FCE module (i.e., modules of squeeze-and-excitation (SE), global context (GC), gather-excite (GE), and Gaussian context transformer (GCT)) to enhance the model performance. Experimental results on the GRAZPEDWRI-DX dataset demonstrate that our proposed YOLOv8 + GC-M3 model improves the mAP val 50 value from 65.78% to 66.32%, outperforming the state-of-the-art (SOTA) model while reducing inference time. Furthermore, our proposed YOLOv8 + SE-M3 model achieves the highest mAP val 50 value of 67.07%, exceed- ing the SOTA performance. The implementation of this work is publicly available at https://github.com/RuiyangJu/FCE-YOLOv8 . |
| 關鍵字 | computer vision; feature contexts excitation; fracture detection; medical image diagnostics; medical image processing; object detection; you only look once (YOLO) |
| 語言 | en |
| ISSN | 1751-9667; 1751-9659 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | GBR |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/129510 ) |