{
  "id": "2205.12705",
  "version": "v1",
  "published": "2022-05-25T12:01:03.000Z",
  "updated": "2022-05-25T12:01:03.000Z",
  "title": "COVID-19 Severity Classification on Chest X-ray Images",
  "authors": [
    "Aditi Sagar",
    "Aman Swaraj",
    "Karan Verma"
  ],
  "categories": [
    "eess.IV",
    "cs.CV"
  ],
  "abstract": "Biomedical imaging analysis combined with artificial intelligence (AI) methods has proven to be quite valuable in order to diagnose COVID-19. So far, various classification models have been used for diagnosing COVID-19. However, classification of patients based on their severity level is not yet analyzed. In this work, we classify covid images based on the severity of the infection. First, we pre-process the X-ray images using a median filter and histogram equalization. Enhanced X-ray images are then augmented using SMOTE technique for achieving a balanced dataset. Pre-trained Resnet50, VGG16 model and SVM classifier are then used for feature extraction and classification. The result of the classification model confirms that compared with the alternatives, with chest X-Ray images, the ResNet-50 model produced remarkable classification results in terms of accuracy (95%), recall (0.94), and F1-Score (0.92), and precision (0.91).",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-05-25T12:01:03.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "chest x-ray images",
      "severity classification",
      "classification model confirms",
      "vgg16 model",
      "artificial intelligence"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}