October 6, 2017 Dataset Open Access

Sara Moccia; Elena De Momi; Leonardo S. Mattos

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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.1003200", 
  "author": [
    {
      "family": "Sara Moccia"
    }, 
    {
      "family": "Elena De Momi"
    }, 
    {
      "family": "Leonardo S. Mattos"
    }
  ], 
  "issued": {
    "date-parts": [
      [
        2017, 
        10, 
        6
      ]
    ]
  }, 
  "abstract": "<p>The dataset in <em><strong>laryngeal dataset.tar </strong></em>contains 1320 patches of healthy and early-stage cancerous laryngeal tissues. The patches (100x100 pixels) were manually extracted from 33 narrow-band laryngoscopic images of 33 different patients affected by laryngeal spinocellular carcinoma (diagnosed after histopathological examination).</p>\n\n<p>Specifically, four tissue classes were considered (330 patches/tissue class): He (healthy tissue), Hbv (tissue with hypertrophic vessels), Le (tissue with leukoplakia) and IPCL (tissue with intrapapillary capillary loops).</p>\n\n<p>The dataset was created for testing the method proposed in <em>Moccia, Sara, et al. \"Confident texture-based laryngeal tissue classification for early stage diagnosis support.\" JOURNAL OF MEDICAL IMAGING 4.03 (2017): 1-10.</em></p>\n\n<p>The folder<em> <strong>laryngeal dataset.tar</strong> </em>contains 3 subfolders (FOLD 1, FOLD 2, FOLD 3), which are the 3 folds used for cross-validation purpose in the tissue classification performance assessment.</p>\n\n<p>Each subfolder contains 4 folders relative to the four tissue classes, i.e., Le, He, Hbv, IPCL.</p>\n\n<p>----------------------------------------------------------------------------------------------------------------------------------------------------------</p>\n\n<p>If you want to use the dataset, please cite <em>Moccia, Sara, et al. \"Confident texture-based laryngeal tissue classification for early stage diagnosis support.\" JOURNAL OF MEDICAL IMAGING 4.03 (2017): 1-10.</em></p>\n\n<p>\u00a0</p>\n\n<p>\u00a0</p>\n\n<p>\u00a0</p>", 
  "title": "Laryngeal dataset", 
  "type": "dataset", 
  "id": "1003200"
}