{
  "id": "2010.01362",
  "version": "v1",
  "published": "2020-10-03T13:57:08.000Z",
  "updated": "2020-10-03T13:57:08.000Z",
  "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"
  ],
  "comment": "Elisha Goldstein, Daphna Keidar, and Daniel Yaron have made an equal contribution and are equal first authors, listed alphabetically",
  "categories": [
    "eess.IV",
    "cs.CV",
    "cs.LG"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-10-03T13:57:08.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep neural networks",
      "x-ray images",
      "classification",
      "learning model",
      "similar dnn-based image embeddings"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}