{
  "id": "2101.11508",
  "version": "v1",
  "published": "2021-01-27T16:07:48.000Z",
  "updated": "2021-01-27T16:07:48.000Z",
  "title": "Effects of Image Size on Deep Learning",
  "authors": [
    "Olivier Rukundo"
  ],
  "comment": "5 pages, 14 figures, 2 table",
  "categories": [
    "cs.CV",
    "cs.LG",
    "eess.IV"
  ],
  "abstract": "The question is: what size of the region of interest is likely to lead to better training outcomes? To answer this: The U-net is used for semantic segmentation. Image interpolation algorithms are used to double the cropped image size and create new datasets. Depending on the selected image interpolation algorithm category, non-original classes are created in the ground truth images thus a filtering strategy is introduced to remove such spurious classes. Evaluation results of effects on the myocardium segmentation and quantification of the myocardial infarction are provided and discussed.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-01-27T16:07:48.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "image size",
      "deep learning",
      "selected image interpolation algorithm category",
      "ground truth images",
      "myocardial infarction"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 5,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}