Automatic detection and classification of peri-prosthetic femur fracture.

Journal: International journal of computer assisted radiology and surgery
Published Date: Feb 14, 2022

Abstract

PURPOSE: Object classification and localization is a key task of computer-aided diagnosis (CAD) tool. Although there have been numerous generic deep learning (DL) models developed for CAD, there is no work in the literature to evaluate their effectiveness when utilized in diagnosing fractures in proximity of joint implants. In this work, we aim to assess the performance of existing classification systems on binary and multi-class problems (fracture types) using plain radiographs. In addition, we evaluated the performance of object detection systems using the one- and two-stage DL architectures.

Authors

  • Asma Alzaid
    School of Electrical and Electronic Engineering, University of Leeds, Leeds, LS2 9JT, UK. scaalz@leeds.ac.uk.
  • Alice Wignall
    Trauma and orthopaedics Leeds, Leeds, UK.
  • Sanja Dogramadzi
    Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK.
  • Hemant Pandit
    Leeds Teaching Hospitals NHS Trust, Leeds, UK.
  • Sheng Quan Xie
    School of Electrical and Electronic Engineering, University of Leeds, Leeds, LS2 9JT, UK. S.Q.Xie@leeds.ac.uk.

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