{
  "id": "2105.14333",
  "version": "v1",
  "published": "2021-05-29T16:12:15.000Z",
  "updated": "2021-05-29T16:12:15.000Z",
  "title": "Covid-19 diagnosis from x-ray using neural networks",
  "authors": [
    "Dinesh J",
    "Mohammed Rhithick A"
  ],
  "comment": "3 Graphs, 1 Figures",
  "categories": [
    "eess.IV",
    "cs.CV",
    "cs.LG"
  ],
  "abstract": "Corona virus or COVID-19 is a pandemic illness, which has influenced more than million of causalities worldwide and infected a few large number of individuals .Innovative instrument empowering quick screening of the COVID-19 contamination with high precision can be critically useful to the medical care experts. The primary clinical device presently being used for the analysis of COVID-19 is the Reverse record polymerase chain response as known as RT-PCR, which is costly, less-delicate and requires specific clinical work force. X-Ray imaging is an effectively available apparatus that can be a great option in the COVID-19 conclusion. This exploration was taken to examine the utility of computerized reasoning in the quick and exact recognition of COVID-19 from chest X-Ray pictures. The point of this paper is to propose a procedure for programmed recognition of COVID-19 from advanced chest X-Ray images applying pre-prepared profound learning calculations while boosting the discovery exactness. The point is to give over-focused on clinical experts a second pair of eyes through a learning picture characterization models. We distinguish an appropriate Convolutional Neural Network-CNN model through beginning similar investigation of a few mainstream CNN models.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-05-29T16:12:15.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "neural networks",
      "images applying pre-prepared profound",
      "chest x-ray images applying",
      "pre-prepared profound learning calculations",
      "reverse record polymerase chain response"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}