{
  "id": "2208.04616",
  "version": "v1",
  "published": "2022-08-09T09:21:22.000Z",
  "updated": "2022-08-09T09:21:22.000Z",
  "title": "EfficientNet for Brain-Lesion classification",
  "authors": [
    "Quoc-Huy Trinh",
    "Trong-Hieu Nguyen Mau",
    "Radmir Zosimov",
    "Minh-Van Nguyen"
  ],
  "categories": [
    "eess.IV",
    "cs.CV",
    "cs.LG"
  ],
  "abstract": "In the development of technology, there are increasing cases of brain disease, there are more treatments proposed and achieved a positive result. However, with Brain-Lesion, the early diagnoses can improve the possibility for successful treatment and can help patients recuperate better. From this reason, Brain-Lesion is one of the controversial topics in medical images analysis nowadays. With the improvement of the architecture, there is a variety of methods that are proposed and achieve competitive scores. In this paper, we proposed a technique that uses efficient-net for 3D images, especially the Efficient-net B0 for Brain-Lesion classification task solution, and achieve the competitive score. Moreover, we also proposed the method to use Multiscale-EfficientNet to classify the slices of the MRI data",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-08-09T09:21:22.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "efficientnet",
      "brain-lesion classification task solution",
      "help patients recuperate better",
      "medical images analysis nowadays",
      "competitive score"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}