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

Autor: Keidar D; ETH Zürich, Department of Computer Science, Rämistrasse 101, 8092, Zürich, Switzerland., Yaron D; Department of Math and Computer Science, Weizmann Institute of Science, Rehovot, Israel., Goldstein E; Bioinformatics Unit, Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel., Shachar Y; Eyeway Vision Ltd., Yoni Netanyahu St 3, Or Yehuda, Israel., Blass A; Department of Math and Computer Science, Weizmann Institute of Science, Rehovot, Israel., Charbinsky L; Department of Radiology, HaEmek Medical Center, Afula, Israel., Aharony I; Department of Radiology, HaEmek Medical Center, Afula, Israel., Lifshitz L; Department of Radiology, HaEmek Medical Center, Afula, Israel., Lumelsky D; Department of Radiology, HaEmek Medical Center, Afula, Israel., Neeman Z; Department of Radiology, HaEmek Medical Center, Afula, Israel., Mizrachi M; Department of Otolaryngology, Head and Neck Surgery, Galilee Medical Center, Nahariya, Israel.; The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel., Hajouj M; Department of Otolaryngology, Head and Neck Surgery, Galilee Medical Center, Nahariya, Israel.; The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel., Eizenbach N; Department of Otolaryngology, Head and Neck Surgery, Galilee Medical Center, Nahariya, Israel.; The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel., Sela E; Department of Otolaryngology, Head and Neck Surgery, Galilee Medical Center, Nahariya, Israel.; The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel., Weiss CS; Cardiothoracic Imaging Unit, Shaare Zedek Medical Center, Jerusalem, Israel., Levin P; Cardiothoracic Imaging Unit, Shaare Zedek Medical Center, Jerusalem, Israel., Benjaminov O; Cardiothoracic Imaging Unit, Shaare Zedek Medical Center, Jerusalem, Israel., Bachar GN; Radiology Department, Rabin Medical Center, Jabotinsky Rd 39, Petah Tikva, Israel.; Sakler School of Medicine, Tel-Aviv University, Ramat Aviv, Tel-Aviv, Israel., Tamir S; Radiology Department, Rabin Medical Center, Jabotinsky Rd 39, Petah Tikva, Israel.; Sakler School of Medicine, Tel-Aviv University, Ramat Aviv, Tel-Aviv, Israel., Rapson Y; Radiology Department, Rabin Medical Center, Jabotinsky Rd 39, Petah Tikva, Israel.; Sakler School of Medicine, Tel-Aviv University, Ramat Aviv, Tel-Aviv, Israel., Suhami D; Radiology Department, Rabin Medical Center, Jabotinsky Rd 39, Petah Tikva, Israel.; Sakler School of Medicine, Tel-Aviv University, Ramat Aviv, Tel-Aviv, Israel., Atar E; Radiology Department, Rabin Medical Center, Jabotinsky Rd 39, Petah Tikva, Israel.; Sakler School of Medicine, Tel-Aviv University, Ramat Aviv, Tel-Aviv, Israel., Dror AA; Department of Otolaryngology, Head and Neck Surgery, Galilee Medical Center, Nahariya, Israel.; The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel., Bogot NR; Cardiothoracic Imaging Unit, Shaare Zedek Medical Center, Jerusalem, Israel., Grubstein A; Radiology Department, Rabin Medical Center, Jabotinsky Rd 39, Petah Tikva, Israel.; Sakler School of Medicine, Tel-Aviv University, Ramat Aviv, Tel-Aviv, Israel., Shabshin N; Department of Radiology, HaEmek Medical Center, Afula, Israel., Elyada YM; Mobileye Vision Technologies, Ltd., Hartom 13, Jerusalem, Israel., Eldar YC; Department of Math and Computer Science, Weizmann Institute of Science, Rehovot, Israel. yonina.eldar@weizmann.ac.il.
Jazyk: angličtina
Zdroj: European radiology [Eur Radiol] 2021 Dec; Vol. 31 (12), pp. 9654-9663. Date of Electronic Publication: 2021 May 29.
DOI: 10.1007/s00330-021-08050-1
Abstrakt: Objectives: 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).
Conclusion: We provide deep learning models, trained and evaluated on CXRs that can assist medical efforts and reduce medical staff workload in handling COVID-19.
Key Points: • 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.
(© 2021. European Society of Radiology.)
Databáze: MEDLINE
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