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, 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.

[1]  Kunio Doi,et al.  Automatic detection of abnormalities in chest radiographs using local texture analysis , 2002, IEEE Transactions on Medical Imaging.

[2]  R. Summers,et al.  Abnormal Chest X-Ray Identification With Generative Adversarial One-Class Classifier , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[3]  Thuy Thanh Nguyen,et al.  Applying Multi-CNNs model for detecting abnormal problem on chest x-ray images , 2018, 2018 10th International Conference on Knowledge and Systems Engineering (KSE).

[4]  Bram van Ginneken,et al.  Clavicle segmentation in chest radiographs , 2012, Medical Image Anal..

[5]  B. van Ginneken,et al.  An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information , 2016, Scientific Reports.

[6]  N. Müller,et al.  Fleischner Society: glossary of terms for thoracic imaging. , 2008, Radiology.

[7]  Q. Tao,et al.  Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases , 2020, Radiology.

[8]  B. M. ter Haar Romeny,et al.  Automatic segmentation of lung fields in chest radiographs. , 2000, Medical physics.

[9]  Mark M Hammer,et al.  Pulmonary Tuberculosis: Role of Radiology in Diagnosis and Management. , 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.

[10]  J. Jacob,et al.  An update on COVID-19 for the radiologist - A British society of Thoracic Imaging statement , 2020, Clinical Radiology.

[11]  Joseph Paul Cohen,et al.  COVID-19 Image Data Collection , 2020, ArXiv.

[12]  Michael A. Colman,et al.  42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society , 2019, IEEE Transactions on Medical Imaging.

[13]  Won Jae Lee,et al.  Role of radiology in diagnosis and management of complications of percutaneous endopyelotomy , 1989 .

[14]  R. Howe,et al.  17th International Conference on Medical Image Computing and Computer-Assisted Intervention. , 2014, Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention.

[15]  Sameer Antani,et al.  Automated Detection of Lung Diseases in Chest X-Rays , 2015 .

[16]  Irene Cheng,et al.  Automated cavity detection of infectious pulmonary tuberculosis in chest radiographs , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Ronald M. Summers,et al.  ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

[18]  Yicheng Fang,et al.  Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR , 2020, Radiology.

[19]  Ömer ACER,et al.  Comparison of RT-PCR , 2020 .

[20]  P. Agarwal,et al.  Chest Imaging Appearance of COVID-19 Infection , 2020, Radiology. Cardiothoracic imaging.

[21]  Michael Grass,et al.  Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification , 2018, Scientific Reports.

[22]  Do-Young Kang,et al.  Medical Image Analysis Using Artificial Intelligence , 2019, Progress in Medical Physics.

[23]  Z. Qin,et al.  Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems , 2019, Scientific Reports.

[24]  Milan Sonka,et al.  Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning , 2020, Medical Image Analysis.

[25]  Jonathan H. Chung,et al.  Essentials for Radiologists on COVID-19: An Update—Radiology Scientific Expert Panel , 2020, Radiology.

[26]  Mingxia Zhang,et al.  Analysis of the Antigenic Properties of Membrane Proteins of Mycobacterium tuberculosis , 2019, Scientific Reports.

[27]  Y. Hu,et al.  Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China , 2020, The Lancet.

[28]  Yifan Yu,et al.  CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison , 2019, AAAI.

[29]  Veronica Sanz,et al.  Developing an App to interpret Chest X-rays to support the diagnosis of respiratory pathology with Artificial Intelligence , 2019, ArXiv.

[30]  B. Ginneken,et al.  Automatic segmentation of lung fields in chest radiographs. , 2000 .

[31]  Anup Basu,et al.  A Hybrid Knowledge-Guided Detection Technique for Screening of Infectious Pulmonary Tuberculosis From Chest Radiographs , 2010, IEEE Transactions on Biomedical Engineering.

[32]  Shih-Chung Benedict Lo,et al.  Lung nodules: improved detection with software that suppresses the rib and clavicle on chest radiographs. , 2011, Radiology.

[33]  A. Karargyris,et al.  Automatic screening for tuberculosis in chest radiographs: a survey. , 2013, Quantitative imaging in medicine and surgery.

[34]  Ronald M. Summers,et al.  ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

[35]  Z. Fayad,et al.  CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV) , 2020, Radiology.

[36]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Bram van Ginneken,et al.  Fusion of Local and Global Detection Systems to Detect Tuberculosis in Chest Radiographs , 2010, MICCAI.

[38]  M. Borgdorff,et al.  High sensitivity of chest radiograph reading by clinical officers in a tuberculosis prevalence survey. , 2011, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[39]  Lawrence Hunter.  Faculty Opinions recommendation of Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems. , 2019 .

[40]  A. F. Adams,et al.  The Survey , 2021, Dyslexia in Higher Education.