Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging

The global pandemic of coronavirus disease 2019 (COVID-19) has resulted in an increased demand for testing, diagnosis, and treatment. Reverse transcription polymerase chain reaction (RT-PCR) is the definitive test for the diagnosis of COVID-19; however, chest X-ray radiography (CXR) is a fast, effec...

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Vydáno v:Frontiers in medicine Ročník 7; s. 572071
Hlavní autoři: Yoo, Seung Hoon, Geng, Hui, Chiu, Tin Lok, Yu, Siu Ki, Cho, Dae Chul, Heo, Jin, Choi, Min Sung, Choi, Il Hyun, Cung Van, Cong, Nhung, Nguen Viet, Min, Byung Jun, Lee, Ho
Médium: Journal Article
Jazyk:angličtina
Vydáno: Lausanne Frontiers Media SA 14.07.2020
Frontiers Media S.A
Témata:
Artificial intelligence
chest X-ray radiography
Classification
Coronaviruses
COVID-19
Datasets
Decision making
Decision trees
Deep learning
Hospitals
image classification
Lung diseases
Lungs
Medicine
neural network
Neural networks
Radiography
Tuberculosis
X-rays
United States > US
ISSN:2296-858X, 2296-858X
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Shrnutí:The global pandemic of coronavirus disease 2019 (COVID-19) has resulted in an increased demand for testing, diagnosis, and treatment. Reverse transcription polymerase chain reaction (RT-PCR) is the definitive test for the diagnosis of COVID-19; however, chest X-ray radiography (CXR) is a fast, effective, and affordable test that identifies the possible COVID-19-related pneumonia. This study investigates the feasibility of using a deep learning-based decision-tree classifier for detecting COVID-19 from CXR images. The proposed classifier comprises three binary decision trees, each trained by a deep learning model with convolution neural network based on the PyTorch frame. The first decision tree classifies the CXR images as normal or abnormal. The second tree identifies the abnormal images that contain signs of tuberculosis, whereas the third does the same for COVID-19. The accuracies of the first and second decision trees are 98 and 80%, respectively, whereas the average accuracy of the third decision tree is 95%. The proposed deep learning-based decision-tree classifier may be used in pre-screening patients to conduct triage and fast-track decision making before RT-PCR results are available.
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Reviewed by: J. Cleofe Yoon, Sheikh Khalifa Specialty Hospital, United Arab Emirates; Axel Künstner, University of Lübeck, Germany; Jeongjin Lee, Soongsil University, South Korea
Edited by: Mahmood Yaseen Hachim, Mohammed Bin Rashid University of Medicine and Health Sciences, United Arab Emirates
This article was submitted to Pulmonary Medicine, a section of the journal Frontiers in Medicine
ISSN:2296-858X
2296-858X
DOI:10.3389/fmed.2020.00427