{
  "id": "2011.05317",
  "version": "v1",
  "published": "2020-11-09T17:37:31.000Z",
  "updated": "2020-11-09T17:37:31.000Z",
  "title": "Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning",
  "authors": [
    "Hammam Alshazly",
    "Christoph Linse",
    "Erhardt Barth",
    "Thomas Martinetz"
  ],
  "categories": [
    "eess.IV",
    "cs.CV"
  ],
  "abstract": "This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopt advanced deep network architectures and propose a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conduct extensive sets of experiments on two CT image datasets, namely the SARS-CoV-2 CT-scan and the COVID19-CT. The obtained results show superior performances for our models compared with previous studies, where our best models achieve average accuracy, precision, sensitivity, specificity and F1 score of 99.4%, 99.6%, 99.8%, 99.6% and 99.4% on the SARS-CoV-2 dataset; and 92.9%, 91.3%, 93.7%, 92.2% and 92.5% on the COVID19-CT dataset, respectively. Furthermore, we apply two visualization techniques to provide visual explanations for the models' predictions. The visualizations show well-separated clusters for CT images of COVID-19 from other lung diseases, and accurate localizations of the COVID-19 associated regions.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-11-09T17:37:31.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "chest ct scans",
      "deep learning",
      "ct image",
      "best models achieve average accuracy",
      "adopt advanced deep network architectures"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}