{
  "id": "2006.13212",
  "version": "v1",
  "published": "2020-06-23T06:50:41.000Z",
  "updated": "2020-06-23T06:50:41.000Z",
  "title": "Automated Detection of COVID-19 from CT Scans Using Convolutional Neural Networks",
  "authors": [
    "Rohit Lokwani",
    "Ashrika Gaikwad",
    "Viraj Kulkarni",
    "Aniruddha Pant",
    "Amit Kharat"
  ],
  "categories": [
    "eess.IV",
    "cs.CV",
    "cs.LG"
  ],
  "abstract": "COVID-19 is an infectious disease that causes respiratory problems similar to those caused by SARS-CoV (2003). Currently, swab samples are being used for its diagnosis. The most common testing method used is the RT-PCR method, which has high specificity but variable sensitivity. AI-based detection has the capability to overcome this drawback. In this paper, we propose a prospective method wherein we use chest CT scans to diagnose the patients for COVID-19 pneumonia. We use a set of open-source images, available as individual CT slices, and full CT scans from a private Indian Hospital to train our model. We build a 2D segmentation model using the U-Net architecture, which gives the output by marking out the region of infection. Our model achieves a sensitivity of 96.428% (95% CI: 88%-100%) and a specificity of 88.39% (95% CI: 82%-94%). Additionally, we derive a logic for converting our slice-level predictions to scan-level, which helps us reduce the false positives.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-06-23T06:50:41.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "convolutional neural networks",
      "automated detection",
      "2d segmentation model",
      "chest ct scans",
      "individual ct slices"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}