{
  "id": "2006.13817",
  "version": "v1",
  "published": "2020-06-22T17:55:16.000Z",
  "updated": "2020-06-22T17:55:16.000Z",
  "title": "Stacked Convolutional Neural Network for Diagnosis of COVID-19 Disease from X-ray Images",
  "authors": [
    "Mahesh Gour",
    "Sweta Jain"
  ],
  "comment": "6 tables, 4 figures",
  "categories": [
    "eess.IV",
    "cs.CV",
    "cs.LG"
  ],
  "abstract": "Automatic and rapid screening of COVID-19 from the chest X-ray images has become an urgent need in this pandemic situation of SARS-CoV-2 worldwide in 2020. However, accurate and reliable screening of patients is a massive challenge due to the discrepancy between COVID-19 and other viral pneumonia in X-ray images. In this paper, we design a new stacked convolutional neural network model for the automatic diagnosis of COVID-19 disease from the chest X-ray images. We obtain different sub-models from the VGG19 and developed a 30-layered CNN model (named as CovNet30) during the training, and obtained sub-models are stacked together using logistic regression. The proposed CNN model combines the discriminating power of the different CNN`s sub-models and classifies chest X-ray images into COVID-19, Normal, and Pneumonia classes. In addition, we generate X-ray images dataset referred to as COVID19CXr, which includes 2764 chest x-ray images of 1768 patients from the three publicly available data repositories. The proposed stacked CNN achieves an accuracy of 92.74%, the sensitivity of 93.33%, PPV of 92.13%, and F1-score of 0.93 for the classification of X-ray images. Our proposed approach shows its superiority over the existing methods for the diagnosis of the COVID-19 from the X-ray images.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-06-22T17:55:16.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "stacked convolutional neural network",
      "chest x-ray images",
      "convolutional neural network model",
      "x-ray images dataset",
      "cnn model"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}