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

Seung Hoon Yoo,Ho Lee,Byung Jun Min,Il Hyun Choi,Jin Heo,Siu Ki Yu,Dae Chul Cho,Min Sung Choi,Tin Lok Chiu,Nguen Viet Nhung,Cong Cung Van,Hui Geng

doi:10.3389/fmed.2020.00427

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.

Highlights

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread from Wuhan to the rest of China and to several other countries since December 2019
COVID-19 is typically confirmed by reverse transcription polymerase chain reaction (RT-PCR)
The sensitivity of RT-PCR may not be high enough for early detection, complicating the treatment of presumptive patients [1, 2]. Chest radiography imaging such as X-ray or computed tomography (CT), which is a routine technique for diagnosing pneumonia, can be performed, and it provides a quick, highly sensitive diagnosis of COVID-19 [1]

Summary

Introduction

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread from Wuhan to the rest of China and to several other countries since December 2019. COVID-19 is typically confirmed by reverse transcription polymerase chain reaction (RT-PCR). The sensitivity of RT-PCR may not be high enough for early detection, complicating the treatment of presumptive patients [1, 2]. Chest radiography imaging such as X-ray or computed tomography (CT), which is a routine technique for diagnosing pneumonia, can be performed, and it provides a quick, highly sensitive diagnosis of COVID-19 [1]. Chest X-ray (CXR) images show visual indexes associated with COVID-19 [3], and several studies have shown the feasibility of radiography as a detection tool for COVID-19 [4,5,6,7,8]

Methods

Results

Conclusion