{
  "id": "2009.11850",
  "version": "v1",
  "published": "2020-09-24T17:53:17.000Z",
  "updated": "2020-09-24T17:53:17.000Z",
  "title": "ECOVNet: An Ensemble of Deep Convolutional Neural Networks Based on EfficientNet to Detect COVID-19 From Chest X-rays",
  "authors": [
    "Nihad Karim Chowdhury",
    "Md. Muhtadir Rahman",
    "Noortaz Rezoana",
    "Muhammad Ashad Kabir"
  ],
  "categories": [
    "eess.IV",
    "cs.CV"
  ],
  "abstract": "This paper proposed an ensemble of deep convolutional neural networks (CNN) based on EfficientNet, named ECOVNet, to detect COVID-19 using a large chest X-ray data set. At first, the open-access large chest X-ray collection is augmented, and then ImageNet pre-trained weights for EfficientNet is transferred with some customized fine-tuning top layers that are trained, followed by an ensemble of model snapshots to classify chest X-rays corresponding to COVID-19, normal, and pneumonia. The predictions of the model snapshots, which are created during a single training, are combined through two ensemble strategies, i.e., hard ensemble and soft ensemble to ameliorate classification performance and generalization in the related task of classifying chest X-rays.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-09-24T17:53:17.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep convolutional neural networks",
      "efficientnet",
      "large chest x-ray data set",
      "open-access large chest x-ray collection"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}