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

Published

Frontiers in Medicine, 2020, 7: 427

Authors

Seung Hoon Yoo¹, Tin Lok Chiu¹, GengHui Geng¹, Siu Ki Yu¹, Dae Chul Cho², Jin Heo², Min Sung Choi², Il Hyun Cho², Cong Cung Van³, Nguen Viet Nhung³, Byung Jun Min⁴, and Ho Lee⁵

Affiliations

¹Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong
²Artificial Intelligent Research Lab, Radisen, Seoul, South Korea
³Vietnam National Lung Hospital, Hanoi, Vietnam
⁴Department of Radiation Oncology, Chungbuk National University Hospital, Cheongju, South Korea
⁵Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea

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.