{
  "id": "1410.8576",
  "version": "v1",
  "published": "2014-10-30T22:14:18.000Z",
  "updated": "2014-10-30T22:14:18.000Z",
  "title": "An ensemble-based system for automatic screening of diabetic retinopathy",
  "authors": [
    "Balint Antal",
    "Andras Hajdu"
  ],
  "journal": "Knowledge-Based Systems, Elsevier, Volume 60, April 2014, Pages 20-27",
  "doi": "10.1016/j.knosys.2013.12.023",
  "categories": [
    "cs.CV",
    "cs.LG",
    "stat.AP",
    "stat.ML"
  ],
  "abstract": "In this paper, an ensemble-based method for the screening of diabetic retinopathy (DR) is proposed. This approach is based on features extracted from the output of several retinal image processing algorithms, such as image-level (quality assessment, pre-screening, AM/FM), lesion-specific (microaneurysms, exudates) and anatomical (macula, optic disc) components. The actual decision about the presence of the disease is then made by an ensemble of machine learning classifiers. We have tested our approach on the publicly available Messidor database, where 90% sensitivity, 91% specificity and 90% accuracy and 0.989 AUC are achieved in a disease/no-disease setting. These results are highly competitive in this field and suggest that retinal image processing is a valid approach for automatic DR screening.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-10-30T22:14:18.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "diabetic retinopathy",
      "ensemble-based system",
      "automatic screening",
      "retinal image processing algorithms",
      "optic disc"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2014arXiv1410.8576A"
    }
  }
}