Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection.

Journal: Biomedical engineering online
Published Date: Nov 25, 2020

Abstract

BACKGROUND: The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs.

Authors

  • Lal Hussain
    Department of Computer Sciences & Information Technology, University of Azad Jammu and Kashmir, City Campus 13100, Muzaffarabad, Azad Kashmir, Pakistan.
  • Tony Nguyen
    Department of Radiology, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA.
  • Haifang Li
    Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY.
  • Adeel A Abbasi
    Department of Computer Science and IT, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad, 13100, Azad Kashmir, Pakistan.
  • Kashif J Lone
    Department of Computer Science and IT, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad, 13100, Azad Kashmir, Pakistan.
  • Zirun Zhao
    Department of Radiology, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA.
  • Mahnoor Zaib
    Department of Computer Science and IT, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam, 13230, Azad Kashmir, Pakistan.
  • Anne Chen
    Department of Radiology, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA.
  • Tim Q Duong
    Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, United States of America.

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