Fine-Tuned Deep Convolutional Networks for the Detection of Femoral Neck Fractures on Pelvic Radiographs: A Multicenter Dataset Validation

In this study, we aim to provide a deep convolutional network based femoral neck fracture detection system on radiographs for emergency patients. We retrospectively collected 1,491 frontal pelvic radiographs from three institutions and assigned them to the following data sets: primary dataset (710 r...

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Veröffentlicht in:	IEEE access 2021, Vol.9, p.78495-78503
Hauptverfasser:	Mu, Lin, Qu, Taiping, Dong, Dong, Li, Xiuli, Pei, Yun, Wang, Yuchong, Shi, Guangyao, Li, Yongrui, He, Fujin, Zhang, Huimao
Format:	Artikel
Sprache:	eng
Schlagworte:	convolutional neural network Datasets Diagnostic radiography Feature extraction Femoral neck fractures fine-tuning Fractures Hip Hospitals Model accuracy Neck Object recognition Radiographs Radiography Radiology Sensitivity small sample Test sets Training
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