COVID-19 Classification of X-ray Images Using Deep Neural Networks

• URL: http://arxiv.org/abs/2010.01362v2
• Date: Wed, 7 Oct 2020 08:28:41 GMT
• Title: COVID-19 Classification of X-ray Images Using Deep Neural Networks
• Authors: Elisha Goldstein, Daphna Keidar, Daniel Yaron, 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, Amiel A Dror, Naama R Bogot, Ahuva Grubstein, Nogah Shabshin, Yishai M Elyada, Yonina C Eldar
• Abstract summary: The purpose of this study is to create and evaluate a machine learning model for diagnosis of COVID-19. A machine learning model was built using a pre-trained deep learning model (ReNet50) and enhanced by data augmentation and lung segmentation. The model was evaluated using accuracy, sensitivity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and of the precision-recall (P-R) curve.
• Score: 36.99143569437537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in the diagnosis and monitoring of patients with COVID-19. Machine learning solutions have been shown to be useful for X-ray analysis and classification in a range of medical contexts. The purpose of this study is to create and evaluate a machine learning model for diagnosis of COVID-19, and to provide a tool for searching for similar patients according to their X-ray scans. In this retrospective study, a classifier was built using a pre-trained deep learning model (ReNet50) and enhanced by data augmentation and lung segmentation to detect COVID-19 in frontal CXR images collected between January 2018 and July 2020 in four hospitals in Israel. A nearest-neighbors algorithm was implemented based on the network results that identifies the images most similar to a given image. The model was evaluated using accuracy, sensitivity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and of the precision-recall (P-R) curve. The dataset sourced for this study includes 2362 CXRs, balanced for positive and negative COVID-19, from 1384 patients (63 +/- 18 years, 552 men). Our model achieved 89.7% (314/350) accuracy and 87.1% (156/179) sensitivity in classification of COVID-19 on a test dataset comprising 15% (350 of 2326) of the original data, with AUC of ROC 0.95 and AUC of the P-R curve 0.94. For each image we retrieve images with the most similar DNN-based image embeddings; these can be used to compare with previous cases.

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