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COVID-19 classification of X-ray images using deep neural networks. 2021

Daphna Keidar, and Daniel Yaron, and Elisha Goldstein, and Yair Shachar, and Ayelet Blass, and Leonid Charbinsky, and Israel Aharony, and Liza Lifshitz, and Dimitri Lumelsky, and Ziv Neeman, and Matti Mizrachi, and Majd Hajouj, and Nethanel Eizenbach, and Eyal Sela, and Chedva S Weiss, and Philip Levin, and Ofer Benjaminov, and Gil N Bachar, and Shlomit Tamir, and Yael Rapson, and Dror Suhami, and Eli Atar, and Amiel A Dror, and Naama R Bogot, and Ahuva Grubstein, and Nogah Shabshin, and Yishai M Elyada, and Yonina C Eldar
ETH Zürich, Department of Computer Science, Rämistrasse 101, 8092, Zürich, Switzerland.

OBJECTIVE In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in diagnosis and monitoring of patients with COVID-19. We propose a deep learning model for detection of COVID-19 from CXRs, as well as a tool for retrieving similar patients according to the model's results on their CXRs. For training and evaluating our model, we collected CXRs from inpatients hospitalized in four different hospitals. METHODS In this retrospective study, 1384 frontal CXRs, of COVID-19 confirmed patients imaged between March and August 2020, and 1024 matching CXRs of non-COVID patients imaged before the pandemic, were collected and used to build a deep learning classifier for detecting patients positive for COVID-19. The classifier consists of an ensemble of pre-trained deep neural networks (DNNS), specifically, ReNet34, ReNet50¸ ReNet152, and vgg16, and is enhanced by data augmentation and lung segmentation. We further implemented a nearest-neighbors algorithm that uses DNN-based image embeddings to retrieve the images most similar to a given image. RESULTS Our model achieved accuracy of 90.3%, (95% CI: 86.3-93.7%) specificity of 90% (95% CI: 84.3-94%), and sensitivity of 90.5% (95% CI: 85-94%) on a test dataset comprising 15% (350/2326) of the original images. The AUC of the ROC curve is 0.96 (95% CI: 0.93-0.97). CONCLUSIONS We provide deep learning models, trained and evaluated on CXRs that can assist medical efforts and reduce medical staff workload in handling COVID-19. CONCLUSIONS • A machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for COVID-19 with accuracy and detection rate above 90%. • A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model's image embeddings.

UI MeSH Term Description Entries
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000086382 COVID-19 A viral disorder generally characterized by high FEVER; COUGH; DYSPNEA; CHILLS; PERSISTENT TREMOR; MUSCLE PAIN; HEADACHE; SORE THROAT; a new loss of taste and/or smell (see AGEUSIA and ANOSMIA) and other symptoms of a VIRAL PNEUMONIA. In severe cases, a myriad of coagulopathy associated symptoms often correlating with COVID-19 severity is seen (e.g., BLOOD COAGULATION; THROMBOSIS; ACUTE RESPIRATORY DISTRESS SYNDROME; SEIZURES; HEART ATTACK; STROKE; multiple CEREBRAL INFARCTIONS; KIDNEY FAILURE; catastrophic ANTIPHOSPHOLIPID ANTIBODY SYNDROME and/or DISSEMINATED INTRAVASCULAR COAGULATION). In younger patients, rare inflammatory syndromes are sometimes associated with COVID-19 (e.g., atypical KAWASAKI SYNDROME; TOXIC SHOCK SYNDROME; pediatric multisystem inflammatory disease; and CYTOKINE STORM SYNDROME). A coronavirus, SARS-CoV-2, in the genus BETACORONAVIRUS is the causative agent. 2019 Novel Coronavirus Disease,2019 Novel Coronavirus Infection,2019-nCoV Disease,2019-nCoV Infection,COVID-19 Pandemic,COVID-19 Pandemics,COVID-19 Virus Disease,COVID-19 Virus Infection,Coronavirus Disease 2019,Coronavirus Disease-19,SARS Coronavirus 2 Infection,SARS-CoV-2 Infection,Severe Acute Respiratory Syndrome Coronavirus 2 Infection,COVID19,2019 nCoV Disease,2019 nCoV Infection,2019-nCoV Diseases,2019-nCoV Infections,COVID 19,COVID 19 Pandemic,COVID 19 Virus Disease,COVID 19 Virus Infection,COVID-19 Virus Diseases,COVID-19 Virus Infections,Coronavirus Disease 19,Disease 2019, Coronavirus,Disease, 2019-nCoV,Disease, COVID-19 Virus,Infection, 2019-nCoV,Infection, COVID-19 Virus,Infection, SARS-CoV-2,Pandemic, COVID-19,SARS CoV 2 Infection,SARS-CoV-2 Infections,Virus Disease, COVID-19,Virus Infection, COVID-19
D000086402 SARS-CoV-2 A species of BETACORONAVIRUS causing atypical respiratory disease (COVID-19) in humans. The organism was first identified in 2019 in Wuhan, China. The natural host is the Chinese intermediate horseshoe bat, RHINOLOPHUS affinis. 2019 Novel Coronavirus,COVID-19 Virus,COVID19 Virus,Coronavirus Disease 2019 Virus,SARS Coronavirus 2,SARS-CoV-2 Virus,Severe Acute Respiratory Syndrome Coronavirus 2,Wuhan Coronavirus,Wuhan Seafood Market Pneumonia Virus,2019-nCoV,2019 Novel Coronaviruses,COVID 19 Virus,COVID-19 Viruses,COVID19 Viruses,Coronavirus 2, SARS,Coronavirus, 2019 Novel,Coronavirus, Wuhan,Novel Coronavirus, 2019,SARS CoV 2 Virus,SARS-CoV-2 Viruses,Virus, COVID-19,Virus, COVID19,Virus, SARS-CoV-2,Viruses, COVID19
D012189 Retrospective Studies Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons. Retrospective Study,Studies, Retrospective,Study, Retrospective
D014965 X-Rays Penetrating electromagnetic radiation emitted when the inner orbital electrons of an atom are excited and release radiant energy. X-ray wavelengths range from 1 pm to 10 nm. Hard X-rays are the higher energy, shorter wavelength X-rays. Soft x-rays or Grenz rays are less energetic and longer in wavelength. The short wavelength end of the X-ray spectrum overlaps the GAMMA RAYS wavelength range. The distinction between gamma rays and X-rays is based on their radiation source. Grenz Ray,Grenz Rays,Roentgen Ray,Roentgen Rays,X Ray,X-Ray,Xray,Radiation, X,X-Radiation,Xrays,Ray, Grenz,Ray, Roentgen,Ray, X,Rays, Grenz,Rays, Roentgen,Rays, X,X Radiation,X Rays,X-Radiations
D016571 Neural Networks, Computer A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming. Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron

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