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

Authors
 Seung Hoon Yoo  ;  Hui Geng  ;  Tin Lok Chiu  ;  Siu Ki Yu  ;  Dae Chul Cho  ;  Jin Heo  ;  Min Sung Choi  ;  Il Hyun Choi  ;  Cong Cung Van  ;  Nguen Viet Nhung  ;  Byung Jun Min  ;  Ho Lee 
Citation
 FRONTIERS IN MEDICINE, Vol.7 : 427, 2020-07 
Journal Title
FRONTIERS IN MEDICINE
Issue Date
2020-07
Keywords
COVID-19 ; chest X-ray radiography ; deep learning ; image classification ; neural network ; tuberculosis
Abstract
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.
Files in This Item:
T202002897.pdf Download
DOI
10.3389/fmed.2020.00427
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiation Oncology (방사선종양학교실) > 1. Journal Papers
Yonsei Authors
Lee, Ho(이호) ORCID logo https://orcid.org/0000-0001-5773-6893
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/179520
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