{
  "id": "1810.05725",
  "version": "v1",
  "published": "2018-10-08T23:44:59.000Z",
  "updated": "2018-10-08T23:44:59.000Z",
  "title": "Neural Network based classification of bone metastasis by primary cacinoma",
  "authors": [
    "Marija Prokopijević",
    "Aleksandar Stančić",
    "Jelena Vasiljević",
    "Željko Stojković",
    "Goran Dimić",
    "Jelena Sopta",
    "Dalibor Ristić",
    "Dhinaharan Nagamalai"
  ],
  "comment": "13 pages, 9 figures",
  "journal": "Computer Science & Information Technology (CS & IT), 7th International Conference on Information Technology Convergence and Services (ITCSE 2018), Vienna, Austria, May 26~27, 2018",
  "doi": "10.5121/csit.2018.80707",
  "categories": [
    "cs.CV",
    "cs.CR"
  ],
  "abstract": "Neural networks have been known for a long time as a tool for different types of classification, but only just in the last decade they have showed their entire power. Along with appearing of hardware that is capable to support demanding matrix operations and parallel algorithms, the neural network, as a universal function approximation framework, turns out to be the most successful classification method widely used in all fields of science. On the other side, multifractal (MF) approach is an efficient way for quantitative description of complex structures [1] such as metastatic carcinoma, which recommends this method as an accurate tool for medical diagnostics. The only part that is missing is classification method. The goal of this research is to describe and apply a feed-forward neural network as an auxiliary diagnostic method for classification of multifractal parameters in order to determine primary cancer.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-10-08T23:44:59.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "neural network",
      "bone metastasis",
      "primary cacinoma",
      "universal function approximation framework",
      "support demanding matrix operations"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 13,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}