{
  "id": "2003.09208",
  "version": "v1",
  "published": "2020-03-20T11:41:28.000Z",
  "updated": "2020-03-20T11:41:28.000Z",
  "title": "Diagnosis of Diabetic Retinopathy in Ethiopia: Before the Deep Learning based Automation",
  "authors": [
    "Misgina Tsighe Hagos"
  ],
  "comment": "Accepted for poster presentation at the Practical Machine Learning for Developing Countries (PML4DC) workshop, ICLR 2020",
  "categories": [
    "eess.IV",
    "cs.CV",
    "cs.LG"
  ],
  "abstract": "Introducing automated Diabetic Retinopathy (DR) diagnosis into Ethiopia is still a challenging task, despite recent reports that present trained Deep Learning (DL) based DR classifiers surpassing manual graders. This is mainly because of the expensive cost of conventional retinal imaging devices used in DL based classifiers. Current approaches that provide mobile based binary classification of DR, and the way towards a cheaper and offline multi-class classification of DR will be discussed in this paper.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-03-20T11:41:28.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep learning",
      "dr classifiers surpassing manual graders",
      "automation",
      "conventional retinal imaging devices",
      "offline multi-class classification"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}