High-performance scaphoid fracture recognition via effectiveness assessment of artificial neural networks

Yu Cheng Tung, Ja Hwung Su*, Yi Wen Liao, Ching Di Chang, Yu Fan Cheng, Wan Ching Chang, Bo Hong Chen

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

5 Scopus citations


Image recognition through the use of deep learning (DL) techniques has recently become a hot topic in many fields. Especially for bioimage informatics, DL-based image recognition has been successfully used in several applications, such as cancer and fracture detection. However, few previous studies have focused on detecting scaphoid fractures, and the related effectiveness is also not significant. Aimed at this issue, in this paper, we present a two-stage method for scaphoid fracture recognition by conducting an effectiveness analysis of numerous state-of-the-art artificial neural networks. In the first stage, the scaphoid bone is extracted from the radiograph using object detection techniques. Based on the object extracted, several convolutional neural networks (CNNs), with or without transfer learning, are utilized to recognize the segmented object. Finally, the analytical details on a real data set are given, in terms of various evaluation metrics, including sensitiv-ity, specificity, precision, F1-score, area under the receiver operating curve (AUC), kappa, and ac-curacy. The experimental results reveal that the CNNs with transfer learning are more effective than those without transfer learning. Moreover, DenseNet201 and ResNet101 are found to be more prom-ising than the other methods, on average. According to the experimental results, DenseNet201 and ResNet101 can be recommended as considerable solutions for scaphoid fracture detection within a bioimage diagnostic system.

Original languageEnglish
Article number8485
JournalApplied Sciences (Switzerland)
Issue number18
StatePublished - 09 2021

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Artificial intelligence
  • Convolutional neural networks
  • Deep learning
  • Image recognition
  • Scaphoid fracture


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