{
  "id": "2303.11935",
  "version": "v1",
  "published": "2023-03-18T12:38:23.000Z",
  "updated": "2023-03-18T12:38:23.000Z",
  "title": "Vision Transformer-based Model for Severity Quantification of Lung Pneumonia Using Chest X-ray Images",
  "authors": [
    "Bouthaina Slika",
    "Fadi Dornaika",
    "Hamid Merdji",
    "Karim Hammoudi"
  ],
  "comment": "This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible",
  "categories": [
    "eess.IV",
    "cs.CV",
    "cs.LG"
  ],
  "abstract": "To develop generic and reliable approaches for diagnosing and assessing the severity of COVID-19 from chest X-rays (CXR), a large number of well-maintained COVID-19 datasets are needed. Existing severity quantification architectures require expensive training calculations to achieve the best results. For healthcare professionals to quickly and automatically identify COVID-19 patients and predict associated severity indicators, computer utilities are needed. In this work, we propose a Vision Transformer (ViT)-based neural network model that relies on a small number of trainable parameters to quantify the severity of COVID-19 and other lung diseases. We present a feasible approach to quantify the severity of CXR, called Vision Transformer Regressor Infection Prediction (ViTReg-IP), derived from a ViT and a regression head. We investigate the generalization potential of our model using a variety of additional test chest radiograph datasets from different open sources. In this context, we performed a comparative study with several competing deep learning analysis methods. The experimental results show that our model can provide peak performance in quantifying severity with high generalizability at a relatively low computational cost. The source codes used in our work are publicly available at https://github.com/bouthainas/ViTReg-IP.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-03-18T12:38:23.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "I.4.0",
      "I.4.9",
      "I.4.7",
      "I.2.0"
    ],
    "keywords": [
      "chest x-ray images",
      "vision transformer-based model",
      "severity quantification",
      "lung pneumonia",
      "test chest radiograph datasets"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}