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A Two-Stage Method for Bone X-Rays Abnormality Detection Using MobileNet Network

  • Hadeer El-SaadawyEmail author
  • Manal Tantawi
  • Howida A. Shedeed
  • Mohamed F. Tolba
Conference paper
  • 107 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)

Abstract

Abnormality in bones is considered to be from the critical medical cases, since the wrong diagnosis could lead to bad side effects. Moreover, exhausted and over loaded doctors may miss some cases. Hence, Computer aided diagnosis systems have a vital role nowadays. This paper presents a method for detecting the fractures in the seven extremity upper bones (shoulder, humerus, forearm, elbow, wrist, hand and finger) using X-ray images. A two-stage classification method based on MobileNet network is proposed. Enhanced X-ray image is fed into the first stage to detect bone type. Thereafter, the bone image is directed according to the result of the first stage to one of seven classifiers (one for each bone type) to detect the abnormality in the bone. MURA dataset is utilized as a performance dataset and average accuracy 73.42% has been achieved after merging the two classification stages.

Keywords

Computer aided diagnosis Medical imaging Deep learning 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hadeer El-Saadawy
    • 1
    Email author
  • Manal Tantawi
    • 1
  • Howida A. Shedeed
    • 1
  • Mohamed F. Tolba
    • 1
  1. 1.Faculty of Computer and Information ScienceAin Shams UniversityCairoEgypt

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