29.05.2021 | COVID-19 | Imaging Informatics and Artificial Intelligence
Zur Zeit gratis
COVID-19 classification of X-ray images using deep neural networks
verfasst von:
Daphna Keidar, Daniel Yaron, Elisha Goldstein, Yair Shachar, Ayelet Blass, Leonid Charbinsky, Israel Aharony, Liza Lifshitz, Dimitri Lumelsky, Ziv Neeman, Matti Mizrachi, Majd Hajouj, Nethanel Eizenbach, Eyal Sela, Chedva S. Weiss, Philip Levin, Ofer Benjaminov, Gil N. Bachar, Shlomit Tamir, Yael Rapson, Dror Suhami, Eli Atar, Amiel A. Dror, Naama R. Bogot, Ahuva Grubstein, Nogah Shabshin, Yishai M. Elyada, Yonina C. Eldar
Erschienen in:
European Radiology
|
Ausgabe 12/2021
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Abstract
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.