Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging
Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging
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DOI：
10.3389/fmed.2020.00427
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发表时间：
2020-07-14
影响因子：
3.9
通讯作者：
Lee, Ho
中科院分区：
文献类型：
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作者：
Yoo, Seung Hoon;Geng, Hui;Lee, Ho
2019冠状病毒病（COVID-19）全球大流行导致对检测、诊断和治疗的需求增加。逆转录聚合酶链反应（RT-PCR）是诊断COVID-19的决定性测试;然而，胸部X线摄影（CXR）是一种快速，有效且负担得起的测试，可识别可能的COVID-19相关肺炎。本研究调查了使用基于深度学习的决策树分类器从CXR图像中检测COVID-19的可行性。所提出的分类器包括三个二叉决策树，每个二叉决策树都由基于PyTorch框架的卷积神经网络深度学习模型训练。第一决策树将CXR图像分类为正常或异常。第二棵树识别包含结核病迹象的异常图像，而第三棵树则对COVID-19进行相同的识别。第一和第二决策树的准确率分别为98%和80%，而第三决策树的平均准确率为95%。所提出的基于深度学习的决策树分类器可用于预筛选患者，以在RT-PCR结果可用之前进行分诊和快速决策。
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.