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

The authors of this retrospective study propose a deep learning model for the detection of COVID-19 from chest x-rays (CXRs), as well as a tool for retrieving similar patients according to the model’s results on their CXRs. The data used for training and evaluating this model was collected from inpatients across four different hospitals. The proposed model achieved accuracy of 90.3%, specificity of 90%, and sensitivity of 90.5%. This resulted in the deep learning models, trained and evaluated on CXRs that are able to 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.

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

Authors: 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

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