Comparing stacking ensemble techniques to improve musculoskeletal fracture image classification

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Abstract

Bone fractures are among the main reasons for emergency room admittance and require a rapid response from doctors. Bone fractures can be severe and can lead to permanent disability if not treated correctly and rapidly. Using X-ray imaging in the emergency room to detect fractures is a challenging task that requires an experienced radiologist, a specialist who is not always available. The availability of an automatic tool for image classification can provide a second opinion for doctors operating in the emergency room and reduce the error rate in diagnosis. This study aims to increase the existing state-of-the-art convolutional neural networks’ performance by using various ensemble techniques. In this approach, different CNNs (Convolutional Neural Networks) are used to classify the images; rather than choosing the best one, a stacking ensemble provides a more reliable and robust classifier. The ensemble model outperforms the results of individual CNNs by an average of 10%.

Original languageEnglish
Article number100
Pages (from-to)1-24
Number of pages24
JournalJournal of Imaging
Volume7
Issue number6
DOIs
Publication statusPublished - 21 Jun 2021

Keywords

  • Convolutional neural networks
  • Deep learning
  • Ensemble learning
  • Image classification
  • Medical images
  • Stacking
  • Transfer learning

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