Automated feature detection in dental periapical radiographs by using deep learning.

Journal: Oral surgery, oral medicine, oral pathology and oral radiology
Published Date: Aug 27, 2020

Abstract

OBJECTIVE: The aim of this study was to investigate automated feature detection, segmentation, and quantification of common findings in periapical radiographs (PRs) by using deep learning (DL)-based computer vision techniques.

Authors

  • Hassan Aqeel Khan
    Assistant Professor, College of Computer Science and Engineering, University of Jeddah, Kingdom of Saudi Arabia.
  • Muhammad Ali Haider
    Electrical Engineering student, National University of Sciences and Technology, Islamabad, Pakistan.
  • Hassan Ali Ansari
    National University of Sciences and Technology, Islamabad, Pakistan.
  • Hamna Ishaq
    Electrical Engineering student, National University of Sciences and Technology, Islamabad, Pakistan.
  • Amber Kiyani
    Assistant Professor, Riphah International University, Islamabad, Pakistan. Electronic address: Amber.kiyani@riphah.edu.pk.
  • Kanwal Sohail
    Demonstrator, Riphah International University, Islamabad, Pakistan.
  • Muhammad Muhammad
    Assistant Professor, Riphah International University, Islamabad, Pakistan.
  • Syed Ali Khurram
    Senior Clinical Lecturer, Consultant Oral Pathologist, University of Sheffield, Sheffield, UK.

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