Fully automated determination of the cervical vertebrae maturation stages using deep learning with directional filters.

Journal: PloS one
Published Date: Jul 1, 2022

Abstract

INTRODUCTION: We aim to apply deep learning to achieve fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. We propose an innovative custom-designed deep Convolutional Neural Network (CNN) with a built-in set of novel directional filters that highlight the edges of the Cervical Vertebrae in X-ray images.

Authors

  • Salih Furkan Atici
    Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois, United States of America.
  • Rashid Ansari
    Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois, United States of America.
  • Veerasathpurush Allareddy
    Department Head and Brodie Craniofacial Endowed Chair, Department of Orthodontics - University of Illinois at Chicago College of Dentistry, Chicago, IL, USA.
  • Omar Suhaym
    Department of Oral and Maxillofacial Surgery, College of Dentistry, University of Illinois, College of Dentistry, Chicago, Illinois, United States of America.
  • Ahmet Enis Cetin
    Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois, United States of America.
  • Mohammed H Elnagar

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