{
  "id": "1410.8577",
  "version": "v1",
  "published": "2014-10-30T22:21:02.000Z",
  "updated": "2014-10-30T22:21:02.000Z",
  "title": "An Ensemble-based System for Microaneurysm Detection and Diabetic Retinopathy Grading",
  "authors": [
    "Balint Antal",
    "Andras Hajdu"
  ],
  "journal": "IEEE Transactions on Biomedical Engineering, vol.59, no.6, pp. 1720-1726, June 2012",
  "doi": "10.1109/TBME.2012.2193126",
  "categories": [
    "cs.CV",
    "cs.AI",
    "stat.AP",
    "stat.ML"
  ],
  "abstract": "Reliable microaneurysm detection in digital fundus images is still an open issue in medical image processing. We propose an ensemble-based framework to improve microaneurysm detection. Unlike the well-known approach of considering the output of multiple classifiers, we propose a combination of internal components of microaneurysm detectors, namely preprocessing methods and candidate extractors. We have evaluated our approach for microaneurysm detection in an online competition, where this algorithm is currently ranked as first and also on two other databases. Since microaneurysm detection is decisive in diabetic retinopathy grading, we also tested the proposed method for this task on the publicly available Messidor database, where a promising AUC 0.90 with 0.01 uncertainty is achieved in a 'DR/non-DR'-type classification based on the presence or absence of the microaneurysms.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-10-30T22:21:02.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "diabetic retinopathy grading",
      "ensemble-based system",
      "digital fundus images",
      "reliable microaneurysm detection",
      "open issue"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2014arXiv1410.8577A"
    }
  }
}