Publication: Deep Learning for Midfacial Fracture Detection in CT Images
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Issued Date
2024-01-25
Resource Type
ISSN
09269630
eISSN
18798365
Scopus ID
2-s2.0-85183576645
Pubmed ID
38269714
Journal Title
Studies in Health Technology and Informatics
Volume
310
Start Page
1497
End Page
1498
Rights Holder(s)
SCOPUS
Bibliographic Citation
Studies in Health Technology and Informatics Vol.310 (2024) , 1497-1498
Suggested Citation
Warin K., Vicharueang S., Jantana P., Limprasert W., Thanathornwong B., Suebnukarn S. Deep Learning for Midfacial Fracture Detection in CT Images. Studies in Health Technology and Informatics Vol.310 (2024) , 1497-1498. 1498. doi:10.3233/SHTI231262 Retrieved from: https://hdl.handle.net/20.500.14740/20219
Corresponding Author(s)
Other Contributor(s)
Abstract
This study deploys the deep learning-based object detection algorithms to detect midfacial fractures in computed tomography (CT) images. The object detection models were created using faster R-CNN and RetinaNet from 2,000 CT images. The best detection model, faster R-CNN, yielded an average precision of 0.79 and an area under the curve (AUC) of 0.80. In conclusion, faster R-CNN model has good potential for detecting midfacial fractures in CT images.