{
  "id": "2102.07975",
  "version": "v1",
  "published": "2021-02-16T06:50:17.000Z",
  "updated": "2021-02-16T06:50:17.000Z",
  "title": "Twin Augmented Architectures for Robust Classification of COVID-19 Chest X-Ray Images",
  "authors": [
    "Kartikeya Badola",
    "Sameer Ambekar",
    "Himanshu Pant",
    "Sumit Soman",
    "Anuradha Sural",
    "Rajiv Narang",
    "Suresh Chandra",
    "Jayadeva"
  ],
  "categories": [
    "cs.CV",
    "cs.LG"
  ],
  "abstract": "The gold standard for COVID-19 is RT-PCR, testing facilities for which are limited and not always optimally distributed. Test results are delayed, which impacts treatment. Expert radiologists, one of whom is a co-author, are able to diagnose COVID-19 positivity from Chest X-Rays (CXR) and CT scans, that can facilitate timely treatment. Such diagnosis is particularly valuable in locations lacking radiologists with sufficient expertise and familiarity with COVID-19 patients. This paper has two contributions. One, we analyse literature on CXR based COVID-19 diagnosis. We show that popular choices of dataset selection suffer from data homogeneity, leading to misleading results. We compile and analyse a viable benchmark dataset from multiple existing heterogeneous sources. Such a benchmark is important for realistically testing models. Our second contribution relates to learning from imbalanced data. Datasets for COVID X-Ray classification face severe class imbalance, since most subjects are COVID -ve. Twin Support Vector Machines (Twin SVM) and Twin Neural Networks (Twin NN) have, in recent years, emerged as effective ways of handling skewed data. We introduce a state-of-the-art technique, termed as Twin Augmentation, for modifying popular pre-trained deep learning models. Twin Augmentation boosts the performance of a pre-trained deep neural network without requiring re-training. Experiments show, that across a multitude of classifiers, Twin Augmentation is very effective in boosting the performance of given pre-trained model for classification in imbalanced settings.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-02-16T06:50:17.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "68T07"
    ],
    "keywords": [
      "chest x-ray images",
      "twin augmented architectures",
      "robust classification",
      "x-ray classification face severe",
      "pre-trained deep learning models"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}